Merge pull request #11 from Shunichi09/develop

Add nonlinear sample Env

Environments.md

@@ -6,6 +6,8 @@
 | Two wheeled System (Constant Goal) | x | ✓ | 3 | 2 | 
 | Two wheeled System (Moving Goal) (Coming soon) | x | ✓ | 3 | 2 | 
 | Cartpole (Swing up) | x | ✓ | 4 | 1 | 
+| Nonlinear Sample System Env | x | ✓ | 2 | 1 | 
+
 
 ## [FistOrderLagEnv](PythonLinearNonlinearControl/envs/first_order_lag.py)
 
@@ -54,3 +56,13 @@ mc = 1, mp = 0.2, l = 0.5, g = 9.81
 ### Cost.
 
 <img src="assets/cartpole_score.png" width="300">
+
+## [Nonlinear Sample System Env](PythonLinearNonlinearControl/envs/nonlinear_sample_system.py)
+
+## System equation.
+
+<img src="assets/nonlinear_sample_system.png" width="400">
+
+### Cost.
+
+<img src="assets/nonlinear_sample_system_score.png" width="400">

PythonLinearNonlinearControl/common/utils.py

@@ -1,5 +1,6 @@
 import numpy as np
 
+
 def rotate_pos(pos, angle):
     """ Transformation the coordinate in the angle
 
@@ -14,6 +15,7 @@ def rotate_pos(pos, angle):
 
     return np.dot(pos, rot_mat.T)
 
+
 def fit_angle_in_range(angles, min_angle=-np.pi, max_angle=np.pi):
     """ Check angle range and correct the range
 
@@ -42,3 +44,75 @@ def fit_angle_in_range(angles, min_angle=-np.pi, max_angle=np.pi):
 
     output = np.minimum(max_angle, np.maximum(min_angle, output))
     return output.reshape(output_shape)
+
+
+def update_state_with_Runge_Kutta(state, u, functions, dt=0.01, batch=True):
+    """ update state in Runge Kutta methods
+    Args:
+        state (array-like): state of system
+        u (array-like): input of system
+        functions (list): update function of each state,
+            each function will be called like func(state, u)
+            We expect that this function returns differential of each state 
+        dt (float): float in seconds
+        batch (bool): state and u is given by batch or not
+
+    Returns:
+        next_state (np.array): next state of system
+
+    Notes:
+        sample of function is as follows:
+
+        def func_x(self, x_1, x_2, u):
+            x_dot = (1. - x_1**2 - x_2**2) * x_2 - x_1 + u
+            return x_dot
+
+        Note that the function return x_dot.
+    """
+    if not batch:
+        state_size = len(state)
+        assert state_size == len(functions), \
+            "Invalid functions length, You need to give the state size functions"
+
+        k0 = np.zeros(state_size)
+        k1 = np.zeros(state_size)
+        k2 = np.zeros(state_size)
+        k3 = np.zeros(state_size)
+
+        for i, func in enumerate(functions):
+            k0[i] = dt * func(state, u)
+
+        for i, func in enumerate(functions):
+            k1[i] = dt * func(state + k0 / 2., u)
+
+        for i, func in enumerate(functions):
+            k2[i] = dt * func(state + k1 / 2., u)
+
+        for i, func in enumerate(functions):
+            k3[i] = dt * func(state + k2, u)
+
+        return state + (k0 + 2. * k1 + 2. * k2 + k3) / 6.
+
+    else:
+        batch_size, state_size = state.shape
+        assert state_size == len(functions), \
+            "Invalid functions length, You need to give the state size functions"
+
+        k0 = np.zeros((batch_size, state_size))
+        k1 = np.zeros((batch_size, state_size))
+        k2 = np.zeros((batch_size, state_size))
+        k3 = np.zeros((batch_size, state_size))
+
+        for i, func in enumerate(functions):
+            k0[:, i] = dt * func(state, u)
+
+        for i, func in enumerate(functions):
+            k1[:, i] = dt * func(state + k0 / 2., u)
+
+        for i, func in enumerate(functions):
+            k2[:, i] = dt * func(state + k1 / 2., u)
+
+        for i, func in enumerate(functions):
+            k3[:, i] = dt * func(state + k2, u)
+
+        return state + (k0 + 2. * k1 + 2. * k2 + k3) / 6.

PythonLinearNonlinearControl/configs/cartpole.py

@@ -1,5 +1,6 @@
 import numpy as np
 
+
 class CartPoleConfigModule():
     # parameters
     ENV_NAME = "CartPole-v0"
@@ -12,7 +13,7 @@ class CartPoleConfigModule():
     DT = 0.02
     # cost parameters
     R = np.diag([0.01])  # 0.01 is worked for MPPI and CEM and MPPIWilliams
-                       # 1. is worked for iLQR 
+    # 1. is worked for iLQR
     TERMINAL_WEIGHT = 1.
     Q = None
     Sf = None
@@ -39,40 +40,40 @@ class CartPoleConfigModule():
                 "num_elites": 50,
                 "max_iters": 15,
                 "alpha": 0.3,
-                "init_var":9.,
+                "init_var": 9.,
-                "threshold":0.001
+                "threshold": 0.001
             },
-            "MPPI":{
+            "MPPI": {
-                "beta" : 0.6,
+                "beta": 0.6,
                 "popsize": 5000,
                 "kappa": 0.9,
                 "noise_sigma": 0.5,
             },
-            "MPPIWilliams":{
+            "MPPIWilliams": {
                 "popsize": 5000,
                 "lambda": 1.,
                 "noise_sigma": 0.9,
             },
-           "iLQR":{
+            "iLQR": {
                 "max_iter": 500,
                 "init_mu": 1.,
                 "mu_min": 1e-6,
                 "mu_max": 1e10,
                 "init_delta": 2.,
                 "threshold": 1e-6,
-           },
+            },
-           "DDP":{
+            "DDP": {
                 "max_iter": 500,
                 "init_mu": 1.,
                 "mu_min": 1e-6,
                 "mu_max": 1e10,
                 "init_delta": 2.,
                 "threshold": 1e-6,
-           },
+            },
-           "NMPC-CGMRES":{
+            "NMPC-CGMRES": {
-           },
+            },
-           "NMPC-Newton":{
+            "NMPC-Newton": {
-           },
+            },
         }
 
     @staticmethod
@@ -103,21 +104,21 @@ class CartPoleConfigModule():
         """
 
         if len(x.shape) > 2:
-            return (6. * (x[:, :, 0]**2) \
+            return (6. * (x[:, :, 0]**2)
-                   + 12. * ((np.cos(x[:, :, 2]) + 1.)**2) \
+                    + 12. * ((np.cos(x[:, :, 2]) + 1.)**2)
-                   + 0.1 * (x[:, :, 1]**2) \
+                    + 0.1 * (x[:, :, 1]**2)
-                   + 0.1 *  (x[:, :, 3]**2))[:, :, np.newaxis]
+                    + 0.1 * (x[:, :, 3]**2))[:, :, np.newaxis]
 
         elif len(x.shape) > 1:
-            return (6. * (x[:, 0]**2) \
+            return (6. * (x[:, 0]**2)
-                   + 12. * ((np.cos(x[:, 2]) + 1.)**2) \
+                    + 12. * ((np.cos(x[:, 2]) + 1.)**2)
-                   + 0.1 * (x[:, 1]**2) \
+                    + 0.1 * (x[:, 1]**2)
-                   + 0.1 * (x[:, 3]**2))[:,  np.newaxis]
+                    + 0.1 * (x[:, 3]**2))[:,  np.newaxis]
 
         return 6. * (x[0]**2) \
-               + 12. * ((np.cos(x[2]) + 1.)**2) \
+            + 12. * ((np.cos(x[2]) + 1.)**2) \
-               + 0.1 * (x[1]**2) \
+            + 0.1 * (x[1]**2) \
-               + 0.1 * (x[3]**2)
+            + 0.1 * (x[3]**2)
 
     @staticmethod
     def terminal_state_cost_fn(terminal_x, terminal_g_x):
@@ -134,18 +135,18 @@ class CartPoleConfigModule():
         """
 
         if len(terminal_x.shape) > 1:
-            return (6. * (terminal_x[:, 0]**2) \
+            return (6. * (terminal_x[:, 0]**2)
-                   + 12. * ((np.cos(terminal_x[:, 2]) + 1.)**2) \
+                    + 12. * ((np.cos(terminal_x[:, 2]) + 1.)**2)
-                   + 0.1 * (terminal_x[:, 1]**2) \
+                    + 0.1 * (terminal_x[:, 1]**2)
-                   + 0.1 * (terminal_x[:, 3]**2))[:, np.newaxis] \
+                    + 0.1 * (terminal_x[:, 3]**2))[:, np.newaxis] \
-                    * CartPoleConfigModule.TERMINAL_WEIGHT
-            
-        return (6. * (terminal_x[0]**2) \
-               + 12. * ((np.cos(terminal_x[2]) + 1.)**2) \
-               + 0.1 * (terminal_x[1]**2) \
-               + 0.1 * (terminal_x[3]**2)) \
                 * CartPoleConfigModule.TERMINAL_WEIGHT
 
+        return (6. * (terminal_x[0]**2)
+                + 12. * ((np.cos(terminal_x[2]) + 1.)**2)
+                + 0.1 * (terminal_x[1]**2)
+                + 0.1 * (terminal_x[3]**2)) \
+            * CartPoleConfigModule.TERMINAL_WEIGHT
+
     @staticmethod
     def gradient_cost_fn_with_state(x, g_x, terminal=False):
         """ gradient of costs with respect to the state
@@ -163,7 +164,7 @@ class CartPoleConfigModule():
             cost_dx1 = 0.2 * x[:, 1]
             cost_dx2 = 24. * (1 + np.cos(x[:, 2])) * -np.sin(x[:, 2])
             cost_dx3 = 0.2 * x[:, 3]
-            cost_dx = np.stack((cost_dx0, cost_dx1,\
+            cost_dx = np.stack((cost_dx0, cost_dx1,
                                 cost_dx2, cost_dx3), axis=1)
             return cost_dx
 
@@ -206,9 +207,9 @@ class CartPoleConfigModule():
             hessian[:, 0, 0] = 12.
             hessian[:, 1, 1] = 0.2
             hessian[:, 2, 2] = 24. * -np.sin(x[:, 2]) \
-                               * (-np.sin(x[:, 2])) \
+                * (-np.sin(x[:, 2])) \
-                               + 24. * (1. + np.cos(x[:, 2])) \
+                + 24. * (1. + np.cos(x[:, 2])) \
-                                 * -np.cos(x[:, 2])
+                * -np.cos(x[:, 2])
             hessian[:, 3, 3] = 0.2
 
             return hessian
@@ -218,9 +219,9 @@ class CartPoleConfigModule():
         hessian[0, 0] = 12.
         hessian[1, 1] = 0.2
         hessian[2, 2] = 24. * -np.sin(x[2]) \
-                         * (-np.sin(x[2])) \
+            * (-np.sin(x[2])) \
-                        + 24. * (1. + np.cos(x[2])) \
+            + 24. * (1. + np.cos(x[2])) \
-                          * -np.cos(x[2])
+            * -np.cos(x[2])
         hessian[3, 3] = 0.2
 
         return hessian[np.newaxis, :, :] * CartPoleConfigModule.TERMINAL_WEIGHT

PythonLinearNonlinearControl/configs/first_order_lag.py

@@ -1,5 +1,6 @@
 import numpy as np
 
+
 class FirstOrderLagConfigModule():
     # parameters
     ENV_NAME = "FirstOrderLag-v0"
@@ -34,42 +35,42 @@ class FirstOrderLagConfigModule():
                 "num_elites": 50,
                 "max_iters": 15,
                 "alpha": 0.3,
-                "init_var":1.,
+                "init_var": 1.,
-                "threshold":0.001
+                "threshold": 0.001
             },
-            "MPPI":{
+            "MPPI": {
-                "beta" : 0.6,
+                "beta": 0.6,
                 "popsize": 5000,
                 "kappa": 0.9,
                 "noise_sigma": 0.5,
             },
-            "MPPIWilliams":{
+            "MPPIWilliams": {
                 "popsize": 5000,
                 "lambda": 1.,
                 "noise_sigma": 0.9,
             },
-           "MPC":{
+            "MPC": {
-           },
+            },
-           "iLQR":{
+            "iLQR": {
                 "max_iter": 500,
                 "init_mu": 1.,
                 "mu_min": 1e-6,
                 "mu_max": 1e10,
                 "init_delta": 2.,
                 "threshold": 1e-6,
-           },
+            },
-           "DDP":{
+            "DDP": {
                 "max_iter": 500,
                 "init_mu": 1.,
                 "mu_min": 1e-6,
                 "mu_max": 1e10,
                 "init_delta": 2.,
                 "threshold": 1e-6,
-           },
+            },
-           "NMPC-CGMRES":{
+            "NMPC-CGMRES": {
-           },
+            },
-           "NMPC-Newton":{
+            "NMPC-Newton": {
-           },
+            },
         }
 
     @staticmethod
@@ -111,7 +112,7 @@ class FirstOrderLagConfigModule():
                 shape(pop_size, pred_len)
         """
         return ((terminal_x - terminal_g_x)**2) \
-                * np.diag(FirstOrderLagConfigModule.Sf)
+            * np.diag(FirstOrderLagConfigModule.Sf)
 
     @staticmethod
     def gradient_cost_fn_with_state(x, g_x, terminal=False):
@@ -128,8 +129,8 @@ class FirstOrderLagConfigModule():
         if not terminal:
             return 2. * (x - g_x) * np.diag(FirstOrderLagConfigModule.Q)
 
-        return (2. * (x - g_x) \
+        return (2. * (x - g_x)
-            * np.diag(FirstOrderLagConfigModule.Sf))[np.newaxis, :]
+                * np.diag(FirstOrderLagConfigModule.Sf))[np.newaxis, :]
 
     @staticmethod
     def gradient_cost_fn_with_input(x, u):

PythonLinearNonlinearControl/configs/make_configs.py

@@ -1,6 +1,8 @@
 from .first_order_lag import FirstOrderLagConfigModule
 from .two_wheeled import TwoWheeledConfigModule
 from .cartpole import CartPoleConfigModule
+from .nonlinear_sample_system import NonlinearSampleSystemConfigModule
+
 
 def make_config(args):
     """
@@ -13,3 +15,5 @@ def make_config(args):
         return TwoWheeledConfigModule()
     elif args.env == "CartPole":
         return CartPoleConfigModule()
+    elif args.env == "NonlinearSample":
+        return NonlinearSampleSystemConfigModule()

PythonLinearNonlinearControl/configs/nonlinear_sample_system.py

@@ -0,0 +1,219 @@
+import numpy as np
+
+
+class NonlinearSampleSystemConfigModule():
+    # parameters
+    ENV_NAME = "NonlinearSampleSystem-v0"
+    PLANNER_TYPE = "Const"
+    TYPE = "Nonlinear"
+    TASK_HORIZON = 2500
+    PRED_LEN = 10
+    STATE_SIZE = 2
+    INPUT_SIZE = 1
+    DT = 0.01
+    R = np.diag([0.01])
+    Q = None
+    Sf = None
+    # bounds
+    INPUT_LOWER_BOUND = np.array([-0.5])
+    INPUT_UPPER_BOUND = np.array([0.5])
+
+    def __init__(self):
+        """
+        """
+        # opt configs
+        self.opt_config = {
+            "Random": {
+                "popsize": 5000
+            },
+            "CEM": {
+                "popsize": 500,
+                "num_elites": 50,
+                "max_iters": 15,
+                "alpha": 0.3,
+                "init_var": 9.,
+                "threshold": 0.001
+            },
+            "MPPI": {
+                "beta": 0.6,
+                "popsize": 5000,
+                "kappa": 0.9,
+                "noise_sigma": 0.5,
+            },
+            "MPPIWilliams": {
+                "popsize": 5000,
+                "lambda": 1.,
+                "noise_sigma": 0.9,
+            },
+            "iLQR": {
+                "max_iter": 500,
+                "init_mu": 1.,
+                "mu_min": 1e-6,
+                "mu_max": 1e10,
+                "init_delta": 2.,
+                "threshold": 1e-6,
+            },
+            "DDP": {
+                "max_iter": 500,
+                "init_mu": 1.,
+                "mu_min": 1e-6,
+                "mu_max": 1e10,
+                "init_delta": 2.,
+                "threshold": 1e-6,
+            },
+            "NMPC-CGMRES": {
+            },
+            "NMPC-Newton": {
+            },
+        }
+
+    @staticmethod
+    def input_cost_fn(u):
+        """ input cost functions
+
+        Args:
+            u (numpy.ndarray): input, shape(pred_len, input_size)
+                or shape(pop_size, pred_len, input_size)
+        Returns:
+            cost (numpy.ndarray): cost of input, shape(pred_len, input_size) or
+                shape(pop_size, pred_len, input_size)
+        """
+        return (u**2) * np.diag(NonlinearSampleSystemConfigModule.R)
+
+    @staticmethod
+    def state_cost_fn(x, g_x):
+        """ state cost function
+
+        Args:
+            x (numpy.ndarray): state, shape(pred_len, state_size)
+                or shape(pop_size, pred_len, state_size)
+            g_x (numpy.ndarray): goal state, shape(pred_len, state_size)
+                or shape(pop_size, pred_len, state_size)
+        Returns:
+            cost (numpy.ndarray): cost of state, shape(pred_len, 1) or
+                shape(pop_size, pred_len, 1)
+        """
+
+        if len(x.shape) > 2:
+            return (0.5 * (x[:, :, 0]**2) +
+                    0.5 * (x[:, :, 1]**2))[:, :, np.newaxis]
+
+        elif len(x.shape) > 1:
+            return (0.5 * (x[:, 0]**2) + 0.5 * (x[:, 1]**2))[:, np.newaxis]
+
+        return 0.5 * (x[0]**2) + 0.5 * (x[1]**2)
+
+    @ staticmethod
+    def terminal_state_cost_fn(terminal_x, terminal_g_x):
+        """
+
+        Args:
+            terminal_x (numpy.ndarray): terminal state,
+                shape(state_size, ) or shape(pop_size, state_size)
+            terminal_g_x (numpy.ndarray): terminal goal state,
+                shape(state_size, ) or shape(pop_size, state_size)
+        Returns:
+            cost (numpy.ndarray): cost of state, shape(pred_len, ) or
+                shape(pop_size, pred_len)
+        """
+
+        if len(terminal_x.shape) > 1:
+            return (0.5 * (terminal_x[:, 0]**2) +
+                    0.5 * (terminal_x[:, 1]**2))[:, np.newaxis]
+
+        return 0.5 * (terminal_x[0]**2) + 0.5 * (terminal_x[1]**2)
+
+    @ staticmethod
+    def gradient_cost_fn_with_state(x, g_x, terminal=False):
+        """ gradient of costs with respect to the state
+
+        Args:
+            x (numpy.ndarray): state, shape(pred_len, state_size)
+            g_x (numpy.ndarray): goal state, shape(pred_len, state_size)
+
+        Returns:
+            l_x (numpy.ndarray): gradient of cost, shape(pred_len, state_size)
+                or shape(1, state_size)
+        """
+        if not terminal:
+            cost_dx0 = x[:, 0]
+            cost_dx1 = x[:, 1]
+            cost_dx = np.stack((cost_dx0, cost_dx1), axis=1)
+            return cost_dx
+
+        cost_dx0 = x[0]
+        cost_dx1 = x[1]
+        cost_dx = np.array([[cost_dx0, cost_dx1]])
+
+        return cost_dx
+
+    @ staticmethod
+    def gradient_cost_fn_with_input(x, u):
+        """ gradient of costs with respect to the input
+
+        Args:
+            x (numpy.ndarray): state, shape(pred_len, state_size)
+            u (numpy.ndarray): goal state, shape(pred_len, input_size)
+        Returns:
+            l_u (numpy.ndarray): gradient of cost, shape(pred_len, input_size)
+        """
+        return 2. * u * np.diag(NonlinearSampleSystemConfigModule.R)
+
+    @ staticmethod
+    def hessian_cost_fn_with_state(x, g_x, terminal=False):
+        """ hessian costs with respect to the state
+
+        Args:
+            x (numpy.ndarray): state, shape(pred_len, state_size)
+            g_x (numpy.ndarray): goal state, shape(pred_len, state_size)
+        Returns:
+            l_xx (numpy.ndarray): gradient of cost,
+                shape(pred_len, state_size, state_size) or
+                shape(1, state_size, state_size) or
+        """
+        if not terminal:
+            (pred_len, state_size) = x.shape
+            hessian = np.eye(state_size)
+            hessian = np.tile(hessian, (pred_len, 1, 1))
+            hessian[:, 0, 0] = 1.
+            hessian[:, 1, 1] = 1.
+
+            return hessian
+
+        state_size = len(x)
+        hessian = np.eye(state_size)
+        hessian[0, 0] = 1.
+        hessian[1, 1] = 1.
+
+        return hessian[np.newaxis, :, :]
+
+    @ staticmethod
+    def hessian_cost_fn_with_input(x, u):
+        """ hessian costs with respect to the input
+
+        Args:
+            x (numpy.ndarray): state, shape(pred_len, state_size)
+            u (numpy.ndarray): goal state, shape(pred_len, input_size)
+        Returns:
+            l_uu (numpy.ndarray): gradient of cost,
+                shape(pred_len, input_size, input_size)
+        """
+        (pred_len, _) = u.shape
+
+        return np.tile(NonlinearSampleSystemConfigModule.R, (pred_len, 1, 1))
+
+    @ staticmethod
+    def hessian_cost_fn_with_input_state(x, u):
+        """ hessian costs with respect to the state and input
+
+        Args:
+            x (numpy.ndarray): state, shape(pred_len, state_size)
+            u (numpy.ndarray): goal state, shape(pred_len, input_size)
+        Returns:
+            l_ux (numpy.ndarray): gradient of cost ,
+                shape(pred_len, input_size, state_size)
+        """
+        (_, state_size) = x.shape
+        (pred_len, input_size) = u.shape
+
+        return np.zeros((pred_len, input_size, state_size))

PythonLinearNonlinearControl/configs/two_wheeled.py

@@ -4,6 +4,7 @@ from matplotlib.axes import Axes
 from ..plotters.plot_objs import square_with_angle, square
 from ..common.utils import fit_angle_in_range
 
+
 class TwoWheeledConfigModule():
     # parameters
     ENV_NAME = "TwoWheeled-v0"
@@ -46,40 +47,40 @@ class TwoWheeledConfigModule():
                 "num_elites": 50,
                 "max_iters": 15,
                 "alpha": 0.3,
-                "init_var":1.,
+                "init_var": 1.,
-                "threshold":0.001
+                "threshold": 0.001
             },
-            "MPPI":{
+            "MPPI": {
-                "beta" : 0.6,
+                "beta": 0.6,
                 "popsize": 5000,
                 "kappa": 0.9,
                 "noise_sigma": 0.5,
             },
-            "MPPIWilliams":{
+            "MPPIWilliams": {
                 "popsize": 5000,
                 "lambda": 1,
                 "noise_sigma": 1.,
             },
-           "iLQR":{
+            "iLQR": {
                 "max_iter": 500,
                 "init_mu": 1.,
                 "mu_min": 1e-6,
                 "mu_max": 1e10,
                 "init_delta": 2.,
                 "threshold": 1e-6,
-           },
+            },
-           "DDP":{
+            "DDP": {
                 "max_iter": 500,
                 "init_mu": 1.,
                 "mu_min": 1e-6,
                 "mu_max": 1e10,
                 "init_delta": 2.,
                 "threshold": 1e-6,
-           },
+            },
-           "NMPC-CGMRES":{
+            "NMPC-CGMRES": {
-           },
+            },
-           "NMPC-Newton":{
+            "NMPC-Newton": {
-           },
+            },
         }
 
     @staticmethod
@@ -142,8 +143,8 @@ class TwoWheeledConfigModule():
             cost (numpy.ndarray): cost of state, shape(pred_len, ) or
                 shape(pop_size, pred_len)
         """
-        terminal_diff = TwoWheeledConfigModule.fit_diff_in_range(terminal_x \
+        terminal_diff = TwoWheeledConfigModule.fit_diff_in_range(terminal_x
-                                                        - terminal_g_x)
+                                                                 - terminal_g_x)
 
         return ((terminal_diff)**2) * np.diag(TwoWheeledConfigModule.Sf)
 
@@ -164,8 +165,8 @@ class TwoWheeledConfigModule():
         if not terminal:
             return 2. * (diff) * np.diag(TwoWheeledConfigModule.Q)
 
-        return (2. * (diff) \
+        return (2. * (diff)
-            * np.diag(TwoWheeledConfigModule.Sf))[np.newaxis, :]
+                * np.diag(TwoWheeledConfigModule.Sf))[np.newaxis, :]
 
     @staticmethod
     def gradient_cost_fn_with_input(x, u):

PythonLinearNonlinearControl/controllers/cem.py

@@ -8,6 +8,7 @@ from ..envs.cost import calc_cost
 
 logger = getLogger(__name__)
 
+
 class CEM(Controller):
     """ Cross Entropy Method for linear and nonlinear method
 
@@ -19,6 +20,7 @@ class CEM(Controller):
         using probabilistic dynamics models.
         In Advances in Neural Information Processing Systems (pp. 4754-4765).
     """
+
     def __init__(self, config, model):
         super(CEM, self).__init__(config, model)
 
@@ -50,13 +52,13 @@ class CEM(Controller):
         self.input_cost_fn = config.input_cost_fn
 
         # init mean
-        self.init_mean = np.tile((config.INPUT_UPPER_BOUND \
+        self.init_mean = np.tile((config.INPUT_UPPER_BOUND
                                   + config.INPUT_LOWER_BOUND) / 2.,
                                  self.pred_len)
         self.prev_sol = self.init_mean.copy()
         # init variance
         var = np.ones_like(config.INPUT_UPPER_BOUND) \
-              * config.opt_config["CEM"]["init_var"]
+            * config.opt_config["CEM"]["init_var"]
         self.init_var = np.tile(var, self.pred_len)
 
         # save
@@ -86,7 +88,7 @@ class CEM(Controller):
 
         # make distribution
         X = stats.truncnorm(-1, 1,
-                            loc=np.zeros_like(mean),\
+                            loc=np.zeros_like(mean),
                             scale=np.ones_like(mean))
 
         while (opt_count < self.max_iters) and np.max(var) > self.epsilon:
@@ -99,15 +101,15 @@ class CEM(Controller):
 
             # sample
             samples = X.rvs(size=[self.pop_size, self.opt_dim]) \
-                      * np.sqrt(constrained_var) \
+                * np.sqrt(constrained_var) \
-                      + mean
+                + mean
 
             # calc cost
             # samples.shape = (pop_size, opt_dim)
             costs = self.calc_cost(curr_x,
                                    samples.reshape(self.pop_size,
-                                                    self.pred_len,
+                                                   self.pred_len,
-                                                    self.input_size),
+                                                   self.input_size),
                                    g_xs)
 
             # sort cost

PythonLinearNonlinearControl/controllers/controller.py

@@ -2,9 +2,11 @@ import numpy as np
 
 from ..envs.cost import calc_cost
 
+
 class Controller():
     """ Controller class
     """
+
     def __init__(self, config, model):
         """
         """
@@ -49,7 +51,7 @@ class Controller():
 
         # get particle cost
         costs = calc_cost(pred_xs, samples, g_xs,
-                          self.state_cost_fn, self.input_cost_fn, \
+                          self.state_cost_fn, self.input_cost_fn,
                           self.terminal_state_cost_fn)
 
         return costs
@@ -58,10 +60,12 @@ class Controller():
     def gradient_hamiltonian_x(x, u, lam):
         """ gradient of hamitonian with respect to the state, 
         """
-        raise NotImplementedError("Implement gradient of hamitonian with respect to the state")
+        raise NotImplementedError(
+            "Implement gradient of hamitonian with respect to the state")
 
     @staticmethod
     def gradient_hamiltonian_u(x, u, lam):
         """  gradient of hamitonian with respect to the input
         """
-        raise NotImplementedError("Implement gradient of hamitonian with respect to the input")
+        raise NotImplementedError(
+            "Implement gradient of hamitonian with respect to the input")

PythonLinearNonlinearControl/controllers/ddp.py

@@ -8,6 +8,7 @@ from ..envs.cost import calc_cost
 
 logger = getLogger(__name__)
 
+
 class DDP(Controller):
     """ Differential Dynamic Programming
 
@@ -18,6 +19,7 @@ class DDP(Controller):
         https://github.com/studywolf/control, and
         https://github.com/anassinator/ilqr
     """
+
     def __init__(self, config, model):
         """
         """
@@ -92,13 +94,13 @@ class DDP(Controller):
             # forward
             if update_sol == True:
                 pred_xs, cost, f_x, f_u, f_xx, f_ux, f_uu,\
-                l_x, l_xx, l_u, l_uu, l_ux = \
+                    l_x, l_xx, l_u, l_uu, l_ux = \
                     self.forward(curr_x, g_xs, sol)
                 update_sol = False
 
             try:
                 # backward
-                k, K = self.backward(f_x, f_u, f_xx, f_ux, f_uu, \
+                k, K = self.backward(f_x, f_u, f_xx, f_ux, f_uu,
                                      l_x, l_xx, l_u, l_uu, l_ux)
 
                 # line search
@@ -139,7 +141,7 @@ class DDP(Controller):
                 # increase regularization term.
                 self.delta = max(1.0, self.delta) * self.init_delta
                 self.mu = max(self.mu_min, self.mu * self.delta)
-                logger.debug("Update regularization term to {}"\
+                logger.debug("Update regularization term to {}"
                              .format(self.mu))
                 if self.mu >= self.mu_max:
                     logger.debug("Reach Max regularization term")
@@ -183,8 +185,8 @@ class DDP(Controller):
 
         for t in range(pred_len):
             new_sol[t] = sol[t] \
-                         + alpha * k[t] \
+                + alpha * k[t] \
-                         + np.dot(K[t], (new_pred_xs[t] - pred_xs[t]))
+                + np.dot(K[t], (new_pred_xs[t] - pred_xs[t]))
             new_pred_xs[t+1] = self.model.predict_next_state(new_pred_xs[t],
                                                              new_sol[t])
 

PythonLinearNonlinearControl/controllers/ilqr.py

@@ -8,6 +8,7 @@ from ..envs.cost import calc_cost
 
 logger = getLogger(__name__)
 
+
 class iLQR(Controller):
     """ iterative Liner Quadratique Regulator
 
@@ -16,6 +17,7 @@ class iLQR(Controller):
         Intelligent Robots and Systems (pp. 4906-4913). and Study Wolf,
         https://github.com/studywolf/control
     """
+
     def __init__(self, config, model):
         """
         """
@@ -130,7 +132,7 @@ class iLQR(Controller):
                 # increase regularization term.
                 self.delta = max(1.0, self.delta) * self.init_delta
                 self.mu = max(self.mu_min, self.mu * self.delta)
-                logger.debug("Update regularization term to {}"\
+                logger.debug("Update regularization term to {}"
                              .format(self.mu))
                 if self.mu >= self.mu_max:
                     logger.debug("Reach Max regularization term")
@@ -174,8 +176,8 @@ class iLQR(Controller):
 
         for t in range(pred_len):
             new_sol[t] = sol[t] \
-                         + alpha * k[t] \
+                + alpha * k[t] \
-                         + np.dot(K[t], (new_pred_xs[t] - pred_xs[t]))
+                + np.dot(K[t], (new_pred_xs[t] - pred_xs[t]))
             new_pred_xs[t+1] = self.model.predict_next_state(new_pred_xs[t],
                                                              new_sol[t])
 

PythonLinearNonlinearControl/controllers/make_controllers.py

@@ -6,6 +6,7 @@ from .mppi_williams import MPPIWilliams
 from .ilqr import iLQR
 from .ddp import DDP
 
+
 def make_controller(args, config, model):
 
     if args.controller_type == "MPC":

PythonLinearNonlinearControl/controllers/mpc.py

@@ -9,6 +9,7 @@ from ..envs.cost import calc_cost
 
 logger = getLogger(__name__)
 
+
 class LinearMPC(Controller):
     """ Model Predictive Controller for linear model
 
@@ -21,6 +22,7 @@ class LinearMPC(Controller):
     Ref:
         Maciejowski, J. M. (2002). Predictive control: with constraints.
     """
+
     def __init__(self, config, model):
         """
         Args:
@@ -77,7 +79,7 @@ class LinearMPC(Controller):
         for _ in range(self.pred_len - 1):
             temp_mat = np.matmul(A_factorials[-1], self.A)
             self.phi_mat = np.vstack((self.phi_mat, temp_mat))
-            A_factorials.append(temp_mat) # after we use this factorials
+            A_factorials.append(temp_mat)  # after we use this factorials
 
         self.gamma_mat = self.B.copy()
         gammma_mat_temp = self.B.copy()
@@ -91,8 +93,8 @@ class LinearMPC(Controller):
 
         for i in range(self.pred_len - 1):
             temp_mat = np.zeros_like(self.gamma_mat)
-            temp_mat[int((i + 1)*self.state_size): , :] =\
+            temp_mat[int((i + 1)*self.state_size):, :] =\
-                self.gamma_mat[:-int((i + 1)*self.state_size) , :]
+                self.gamma_mat[:-int((i + 1)*self.state_size), :]
 
             self.theta_mat = np.hstack((self.theta_mat, temp_mat))
 
@@ -114,7 +116,7 @@ class LinearMPC(Controller):
 
             for i in range(self.pred_len - 1):
                 for j in range(self.input_size):
-                    temp_F[j * 2: (j + 1) * 2,\
+                    temp_F[j * 2: (j + 1) * 2,
                            ((i+1) * self.input_size) + j] = np.array([1., -1.])
                 self.F = np.vstack((self.F, temp_F))
 
@@ -187,14 +189,14 @@ class LinearMPC(Controller):
 
         # using cvxopt
         def optimized_func(dt_us):
-            return (np.dot(dt_us, np.dot(H, dt_us.reshape(-1, 1))) \
+            return (np.dot(dt_us, np.dot(H, dt_us.reshape(-1, 1)))
                     - np.dot(G.T, dt_us.reshape(-1, 1)))[0]
 
         # constraint
         lb = np.array([-np.inf for _ in range(len(ub))])  # one side cons
         cons = LinearConstraint(A, lb, ub)
         # solve
-        opt_sol = minimize(optimized_func, self.prev_sol.flatten(),\
+        opt_sol = minimize(optimized_func, self.prev_sol.flatten(),
                            constraints=[cons])
         opt_dt_us = opt_sol.x
 
@@ -213,7 +215,7 @@ class LinearMPC(Controller):
         """
 
         # to dt form
-        opt_dt_u_seq = np.cumsum(opt_dt_us.reshape(self.pred_len,\
+        opt_dt_u_seq = np.cumsum(opt_dt_us.reshape(self.pred_len,
                                                    self.input_size),
                                  axis=0)
         self.prev_sol = opt_dt_u_seq.copy()
@@ -226,8 +228,8 @@ class LinearMPC(Controller):
         # check costs
         costs = self.calc_cost(curr_x,
                                opt_u_seq.reshape(1,
-                                                  self.pred_len,
+                                                 self.pred_len,
-                                                  self.input_size),
+                                                 self.input_size),
                                g_xs)
 
         logger.debug("Cost = {}".format(costs))

PythonLinearNonlinearControl/controllers/mppi.py

@@ -8,6 +8,7 @@ from ..envs.cost import calc_cost
 
 logger = getLogger(__name__)
 
+
 class MPPI(Controller):
     """ Model Predictive Path Integral for linear and nonlinear method
 
@@ -18,6 +19,7 @@ class MPPI(Controller):
         Deep Dynamics Models for Learning Dexterous Manipulation.
         arXiv preprint arXiv:1909.11652.
     """
+
     def __init__(self, config, model):
         super(MPPI, self).__init__(config, model)
 
@@ -47,7 +49,7 @@ class MPPI(Controller):
         self.input_cost_fn = config.input_cost_fn
 
         # init mean
-        self.prev_sol = np.tile((config.INPUT_UPPER_BOUND \
+        self.prev_sol = np.tile((config.INPUT_UPPER_BOUND
                                  + config.INPUT_LOWER_BOUND) / 2.,
                                 self.pred_len)
         self.prev_sol = self.prev_sol.reshape(self.pred_len, self.input_size)
@@ -74,23 +76,23 @@ class MPPI(Controller):
         """
         # get noised inputs
         noise = np.random.normal(
-                loc=0, scale=1.0, size=(self.pop_size, self.pred_len,
+            loc=0, scale=1.0, size=(self.pop_size, self.pred_len,
-                                        self.input_size)) * self.noise_sigma
+                                    self.input_size)) * self.noise_sigma
         noised_inputs = noise.copy()
 
         for t in range(self.pred_len):
             if t > 0:
                 noised_inputs[:, t, :] = self.beta \
-                                         * (self.prev_sol[t, :] \
+                    * (self.prev_sol[t, :]
-                                         + noise[:, t, :]) \
+                       + noise[:, t, :]) \
-                                         + (1 - self.beta) \
+                    + (1 - self.beta) \
-                                         * noised_inputs[:, t-1, :]
+                    * noised_inputs[:, t-1, :]
             else:
                 noised_inputs[:, t, :] = self.beta \
-                                         * (self.prev_sol[t, :] \
+                    * (self.prev_sol[t, :]
-                                         + noise[:, t, :]) \
+                       + noise[:, t, :]) \
-                                         + (1 - self.beta) \
+                    + (1 - self.beta) \
-                                         * self.history_u[-1]
+                    * self.history_u[-1]
 
         # clip actions
         noised_inputs = np.clip(
@@ -108,7 +110,7 @@ class MPPI(Controller):
 
         # weight actions
         weighted_inputs = exp_rewards[:, np.newaxis, np.newaxis] \
-                          * noised_inputs
+            * noised_inputs
         sol = np.sum(weighted_inputs, 0) / denom
 
         # update

PythonLinearNonlinearControl/controllers/mppi_williams.py

@@ -8,6 +8,7 @@ from ..envs.cost import calc_cost
 
 logger = getLogger(__name__)
 
+
 class MPPIWilliams(Controller):
     """ Model Predictive Path Integral for linear and nonlinear method
 
@@ -19,6 +20,7 @@ class MPPIWilliams(Controller):
         2017 IEEE International Conference on Robotics and Automation (ICRA),
         Singapore, 2017, pp. 1714-1721.
     """
+
     def __init__(self, config, model):
         super(MPPIWilliams, self).__init__(config, model)
 
@@ -47,7 +49,7 @@ class MPPIWilliams(Controller):
         self.input_cost_fn = config.input_cost_fn
 
         # init mean
-        self.prev_sol = np.tile((config.INPUT_UPPER_BOUND \
+        self.prev_sol = np.tile((config.INPUT_UPPER_BOUND
                                  + config.INPUT_LOWER_BOUND) / 2.,
                                 self.pred_len)
         self.prev_sol = self.prev_sol.reshape(self.pred_len, self.input_size)
@@ -85,7 +87,7 @@ class MPPIWilliams(Controller):
 
         # get particle cost
         costs = calc_cost(pred_xs, samples, g_xs,
-                          self.state_cost_fn, None, \
+                          self.state_cost_fn, None,
                           self.terminal_state_cost_fn)
 
         return costs
@@ -101,8 +103,8 @@ class MPPIWilliams(Controller):
         """
         # get noised inputs
         noise = np.random.normal(
-                loc=0, scale=1.0, size=(self.pop_size, self.pred_len,
+            loc=0, scale=1.0, size=(self.pop_size, self.pred_len,
-                                        self.input_size)) * self.noise_sigma
+                                    self.input_size)) * self.noise_sigma
 
         noised_inputs = self.prev_sol + noise
 
@@ -120,7 +122,7 @@ class MPPIWilliams(Controller):
         # mppi update
         beta = np.min(costs)
         eta = np.sum(np.exp(- 1. / self.lam * (costs - beta)), axis=0) \
-              + 1e-10
+            + 1e-10
 
         # weight
         # eta.shape = (pred_len, input_size)
@@ -128,7 +130,7 @@ class MPPIWilliams(Controller):
 
         # update inputs
         sol = self.prev_sol \
-              + np.sum(weights[:, np.newaxis, np.newaxis] * noise, axis=0)
+            + np.sum(weights[:, np.newaxis, np.newaxis] * noise, axis=0)
 
         # update
         self.prev_sol[:-1] = sol[1:]

PythonLinearNonlinearControl/controllers/random.py

@@ -8,6 +8,7 @@ from ..envs.cost import calc_cost
 
 logger = getLogger(__name__)
 
+
 class RandomShooting(Controller):
     """ Random Shooting Method for linear and nonlinear method
 
@@ -19,6 +20,7 @@ class RandomShooting(Controller):
         using probabilistic dynamics models.
         In Advances in Neural Information Processing Systems (pp. 4754-4765).
     """
+
     def __init__(self, config, model):
         super(RandomShooting, self).__init__(config, model)
 
@@ -65,8 +67,8 @@ class RandomShooting(Controller):
         # calc cost
         costs = self.calc_cost(curr_x,
                                samples.reshape(self.pop_size,
-                                                self.pred_len,
+                                               self.pred_len,
-                                                self.input_size),
+                                               self.input_size),
                                g_xs)
         # solution
         sol = samples[np.argmin(costs)]

PythonLinearNonlinearControl/envs/cartpole.py

@@ -4,6 +4,7 @@ from matplotlib.axes import Axes
 from .env import Env
 from ..plotters.plot_objs import square
 
+
 class CartPoleEnv(Env):
     """ Cartpole Environment
 
@@ -13,13 +14,14 @@ class CartPoleEnv(Env):
         6-832-underactuated-robotics-spring-2009/readings/
         MIT6_832s09_read_ch03.pdf
     """
+
     def __init__(self):
         """
         """
-        self.config = {"state_size" : 4,
+        self.config = {"state_size": 4,
-                       "input_size" : 1,
+                       "input_size": 1,
-                       "dt" : 0.02,
+                       "dt": 0.02,
-                       "max_step" : 500,
+                       "max_step": 500,
                        "input_lower_bound": [-3.],
                        "input_upper_bound": [3.],
                        "mp": 0.2,
@@ -76,33 +78,33 @@ class CartPoleEnv(Env):
         # x
         d_x0 = self.curr_x[1]
         # v_x
-        d_x1 = (u[0] + self.config["mp"] * np.sin(self.curr_x[2]) \
+        d_x1 = (u[0] + self.config["mp"] * np.sin(self.curr_x[2])
-               * (self.config["l"] * (self.curr_x[3]**2) \
+                * (self.config["l"] * (self.curr_x[3]**2)
-                  + self.config["g"] * np.cos(self.curr_x[2]))) \
+                   + self.config["g"] * np.cos(self.curr_x[2]))) \
-               / (self.config["mc"] + self.config["mp"] \
+            / (self.config["mc"] + self.config["mp"]
-                  * (np.sin(self.curr_x[2])**2))
+               * (np.sin(self.curr_x[2])**2))
         # theta
         d_x2 = self.curr_x[3]
 
         # v_theta
-        d_x3 = (-u[0] * np.cos(self.curr_x[2]) \
+        d_x3 = (-u[0] * np.cos(self.curr_x[2])
-                - self.config["mp"] * self.config["l"] * (self.curr_x[3]**2) \
+                - self.config["mp"] * self.config["l"] * (self.curr_x[3]**2)
-                  * np.cos(self.curr_x[2]) * np.sin(self.curr_x[2]) \
+                * np.cos(self.curr_x[2]) * np.sin(self.curr_x[2])
-                - (self.config["mc"] + self.config["mp"]) * self.config["g"] \
+                - (self.config["mc"] + self.config["mp"]) * self.config["g"]
-                   * np.sin(self.curr_x[2])) \
+                * np.sin(self.curr_x[2])) \
-               / (self.config["l"] * (self.config["mc"] + self.config["mp"] \
+            / (self.config["l"] * (self.config["mc"] + self.config["mp"]
-                                      * (np.sin(self.curr_x[2])**2)))
+                                   * (np.sin(self.curr_x[2])**2)))
 
         next_x = self.curr_x +\
-                 np.array([d_x0, d_x1, d_x2, d_x3]) * self.config["dt"] 
+            np.array([d_x0, d_x1, d_x2, d_x3]) * self.config["dt"]
 
         # TODO: costs
         costs = 0.
         costs += 0.1 * np.sum(u**2)
         costs += 6. * self.curr_x[0]**2 \
-                 + 12. * (np.cos(self.curr_x[2]) + 1.)**2 \
+            + 12. * (np.cos(self.curr_x[2]) + 1.)**2 \
-                 + 0.1 * self.curr_x[1]**2 \
+            + 0.1 * self.curr_x[1]**2 \
-                 + 0.1 * self.curr_x[3]**2
+            + 0.1 * self.curr_x[3]**2
 
         # save history
         self.history_x.append(next_x.flatten())
@@ -114,8 +116,8 @@ class CartPoleEnv(Env):
         self.step_count += 1
 
         return next_x.flatten(), costs, \
-               self.step_count > self.config["max_step"], \
+            self.step_count > self.config["max_step"], \
-               {"goal_state" : self.g_x}
+            {"goal_state": self.g_x}
 
     def plot_func(self, to_plot, i=None, history_x=None, history_g_x=None):
         """ plot cartpole object function
@@ -134,10 +136,10 @@ class CartPoleEnv(Env):
 
             imgs["cart"] = to_plot.plot([], [], c="k")[0]
             imgs["pole"] = to_plot.plot([], [], c="k", linewidth=5)[0]
-            imgs["center"] = to_plot.plot([], [], marker="o", c="k",\
+            imgs["center"] = to_plot.plot([], [], marker="o", c="k",
                                           markersize=10)[0]
             # centerline
-            to_plot.plot(np.linspace(-1., 1., num=50), np.zeros(50),\
+            to_plot.plot(np.linspace(-1., 1., num=50), np.zeros(50),
                          c="k", linestyle="dashed")
 
             # set axis
@@ -166,13 +168,13 @@ class CartPoleEnv(Env):
             pole_y (numpy.ndarray): y data of pole 
         """
         # cart
-        cart_x, cart_y = square(curr_x[0], 0.,\
+        cart_x, cart_y = square(curr_x[0], 0.,
                                 self.config["cart_size"], 0.)
 
         # pole
-        pole_x = np.array([curr_x[0], curr_x[0] + self.config["l"] \
+        pole_x = np.array([curr_x[0], curr_x[0] + self.config["l"]
-                                      * np.cos(curr_x[2]-np.pi/2)])
+                           * np.cos(curr_x[2]-np.pi/2)])
-        pole_y = np.array([0., self.config["l"] \
+        pole_y = np.array([0., self.config["l"]
-                               * np.sin(curr_x[2]-np.pi/2)])
+                           * np.sin(curr_x[2]-np.pi/2)])
 
         return cart_x, cart_y, pole_x, pole_y

PythonLinearNonlinearControl/envs/cost.py

@@ -4,6 +4,7 @@ import numpy as np
 
 logger = getLogger(__name__)
 
+
 def calc_cost(pred_xs, input_sample, g_xs,
               state_cost_fn, input_cost_fn, terminal_state_cost_fn):
     """ calculate the cost 
@@ -24,14 +25,15 @@ def calc_cost(pred_xs, input_sample, g_xs,
     # state cost
     state_cost = 0.
     if state_cost_fn is not None:
-        state_pred_par_cost = state_cost_fn(pred_xs[:, 1:-1, :], g_xs[:, 1:-1, :])
+        state_pred_par_cost = state_cost_fn(
+            pred_xs[:, 1:-1, :], g_xs[:, 1:-1, :])
         state_cost = np.sum(np.sum(state_pred_par_cost, axis=-1), axis=-1)
 
     # terminal cost
     terminal_state_cost = 0.
     if terminal_state_cost_fn is not None:
         terminal_state_par_cost = terminal_state_cost_fn(pred_xs[:, -1, :],
-                                                        g_xs[:, -1, :])
+                                                         g_xs[:, -1, :])
         terminal_state_cost = np.sum(terminal_state_par_cost, axis=-1)
 
     # act cost

PythonLinearNonlinearControl/envs/env.py

@@ -1,5 +1,6 @@
 import numpy as np
 
+
 class Env():
     """ Environments class
     Attributes:
@@ -8,6 +9,7 @@ class Env():
         history_x (list[numpy.ndarray]): historty of state, shape(step_count*state_size)
         step_count (int): step count
     """
+
     def __init__(self, config):
         """
         """

PythonLinearNonlinearControl/envs/first_order_lag.py

@@ -3,17 +3,19 @@ import scipy
 from scipy import integrate
 from .env import Env
 
+
 class FirstOrderLagEnv(Env):
     """ First Order Lag System Env
     """
+
     def __init__(self, tau=0.63):
         """
         """
-        self.config = {"state_size" : 4,\
+        self.config = {"state_size": 4,
-                       "input_size" : 2,\
+                       "input_size": 2,
-                       "dt" : 0.05,\
+                       "dt": 0.05,
-                       "max_step" : 500,\
+                       "max_step": 500,
-                       "input_lower_bound": [-0.5, -0.5],\
+                       "input_lower_bound": [-0.5, -0.5],
                        "input_upper_bound": [0.5, 0.5],
                        }
 
@@ -34,13 +36,13 @@ class FirstOrderLagEnv(Env):
         """
         # continuous
         Ac = np.array([[-1./tau, 0., 0., 0.],
-                      [0., -1./tau, 0., 0.],
+                       [0., -1./tau, 0., 0.],
-                      [1., 0., 0., 0.], 
+                       [1., 0., 0., 0.],
-                      [0., 1., 0., 0.]])
+                       [0., 1., 0., 0.]])
         Bc = np.array([[1./tau, 0.],
-                      [0., 1./tau],
+                       [0., 1./tau],
-                      [0., 0.],
+                       [0., 0.],
-                      [0., 0.]])
+                       [0., 0.]])
         # to discrete system
         A = scipy.linalg.expm(dt*Ac)
         # B = np.matmul(np.matmul(scipy.linalg.expm(Ac*dt) -
@@ -94,7 +96,7 @@ class FirstOrderLagEnv(Env):
                     self.config["input_upper_bound"])
 
         next_x = np.matmul(self.A, self.curr_x[:, np.newaxis]) \
-                 + np.matmul(self.B, u[:, np.newaxis])
+            + np.matmul(self.B, u[:, np.newaxis])
 
         # cost
         cost = 0
@@ -111,8 +113,8 @@ class FirstOrderLagEnv(Env):
         self.step_count += 1
 
         return next_x.flatten(), cost, \
-               self.step_count > self.config["max_step"], \
+            self.step_count > self.config["max_step"], \
-               {"goal_state" : self.g_x}
+            {"goal_state": self.g_x}
 
     def plot_func(self, to_plot, i=None, history_x=None, history_g_x=None):
         """

PythonLinearNonlinearControl/envs/make_envs.py

@@ -2,6 +2,8 @@ from .first_order_lag import FirstOrderLagEnv
 from .two_wheeled import TwoWheeledConstEnv
 from .two_wheeled import TwoWheeledTrackEnv
 from .cartpole import CartPoleEnv
+from .nonlinear_sample_system import NonlinearSampleSystemEnv
+
 
 def make_env(args):
 
@@ -13,5 +15,7 @@ def make_env(args):
         return TwoWheeledTrackEnv()
     elif args.env == "CartPole":
         return CartPoleEnv()
+    elif args.env == "NonlinearSample":
+        return NonlinearSampleSystemEnv()
 
     raise NotImplementedError("There is not {} Env".format(args.env))

PythonLinearNonlinearControl/envs/nonlinear_sample_system.py

@@ -0,0 +1,97 @@
+import numpy as np
+import scipy
+from scipy import integrate
+from .env import Env
+from ..common.utils import update_state_with_Runge_Kutta
+
+
+class NonlinearSampleSystemEnv(Env):
+    """ Nonlinear Sample Env
+    """
+
+    def __init__(self):
+        """
+        """
+        self.config = {"state_size": 2,
+                       "input_size": 1,
+                       "dt": 0.01,
+                       "max_step": 2000,
+                       "input_lower_bound": [-0.5],
+                       "input_upper_bound": [0.5],
+                       }
+
+        super(NonlinearSampleSystemEnv, self).__init__(self.config)
+
+    def reset(self, init_x=np.array([2., 0.])):
+        """ reset state
+        Returns:
+            init_x (numpy.ndarray): initial state, shape(state_size, )  
+            info (dict): information
+        """
+        self.step_count = 0
+
+        self.curr_x = np.zeros(self.config["state_size"])
+
+        if init_x is not None:
+            self.curr_x = init_x
+
+        # goal
+        self.g_x = np.array([0., 0.])
+
+        # clear memory
+        self.history_x = []
+        self.history_g_x = []
+
+        return self.curr_x, {"goal_state": self.g_x}
+
+    def step(self, u):
+        """
+        Args:
+            u (numpy.ndarray) : input, shape(input_size, )
+        Returns:
+            next_x (numpy.ndarray): next state, shape(state_size, ) 
+            cost (float): costs
+            done (bool): end the simulation or not
+            info (dict): information 
+        """
+        # clip action
+        u = np.clip(u,
+                    self.config["input_lower_bound"],
+                    self.config["input_upper_bound"])
+
+        functions = [self._func_x_1, self._func_x_2]
+
+        next_x = update_state_with_Runge_Kutta(self.curr_x, u,
+                                               functions, self.config["dt"],
+                                               batch=False)
+
+        # cost
+        cost = 0
+        cost = np.sum(u**2)
+        cost += np.sum((self.curr_x - self.g_x)**2)
+
+        # save history
+        self.history_x.append(next_x.flatten())
+        self.history_g_x.append(self.g_x.flatten())
+
+        # update
+        self.curr_x = next_x.flatten()
+        # update costs
+        self.step_count += 1
+
+        return next_x.flatten(), cost, \
+            self.step_count > self.config["max_step"], \
+            {"goal_state": self.g_x}
+
+    def _func_x_1(self, x, u):
+        x_dot = x[1]
+        return x_dot
+
+    def _func_x_2(self, x, u):
+        x_dot = (1. - x[0]**2 - x[1]**2) * x[1] - x[0] + u[0]
+        return x_dot
+
+    def plot_func(self, to_plot, i=None, history_x=None, history_g_x=None):
+        """
+        """
+        raise ValueError("NonlinearSampleSystemEnv does not have animation")

PythonLinearNonlinearControl/envs/two_wheeled.py

@@ -5,6 +5,7 @@ import matplotlib.pyplot as plt
 from .env import Env
 from ..plotters.plot_objs import circle_with_angle, square, circle
 
+
 def step_two_wheeled_env(curr_x, u, dt, method="Oylar"):
     """ step two wheeled enviroment
 
@@ -28,19 +29,21 @@ def step_two_wheeled_env(curr_x, u, dt, method="Oylar"):
 
     return next_x
 
+
 class TwoWheeledConstEnv(Env):
     """ Two wheeled robot with constant goal Env
     """
+
     def __init__(self):
         """
         """
-        self.config = {"state_size" : 3,\
+        self.config = {"state_size": 3,
-                       "input_size" : 2,\
+                       "input_size": 2,
-                       "dt" : 0.01,\
+                       "dt": 0.01,
-                       "max_step" : 500,\
+                       "max_step": 500,
-                       "input_lower_bound": (-1.5, -3.14),\
+                       "input_lower_bound": (-1.5, -3.14),
-                       "input_upper_bound": (1.5, 3.14),\
+                       "input_upper_bound": (1.5, 3.14),
-                       "car_size": 0.2,\
+                       "car_size": 0.2,
                        "wheel_size": (0.075, 0.015)
                        }
 
@@ -103,8 +106,8 @@ class TwoWheeledConstEnv(Env):
         self.step_count += 1
 
         return next_x.flatten(), costs, \
-               self.step_count > self.config["max_step"], \
+            self.step_count > self.config["max_step"], \
-               {"goal_state" : self.g_x}
+            {"goal_state": self.g_x}
 
     def plot_func(self, to_plot, i=None, history_x=None, history_g_x=None):
         """ plot cartpole object function
@@ -139,7 +142,7 @@ class TwoWheeledConstEnv(Env):
         # set imgs
         # car imgs
         car_x, car_y, car_angle_x, car_angle_y, \
-        left_tire_x, left_tire_y, right_tire_x, right_tire_y = \
+            left_tire_x, left_tire_y, right_tire_x, right_tire_y = \
             self._plot_car(history_x[i])
 
         to_plot["car"].set_data(car_x, car_y)
@@ -160,46 +163,48 @@ class TwoWheeledConstEnv(Env):
                               self.config["car_size"], curr_x[2])
 
         # left tire
-        center_x = (self.config["car_size"] \
+        center_x = (self.config["car_size"]
                     + self.config["wheel_size"][1]) \
-                   * np.cos(curr_x[2]-np.pi/2.) + curr_x[0]
+            * np.cos(curr_x[2]-np.pi/2.) + curr_x[0]
-        center_y = (self.config["car_size"] \
+        center_y = (self.config["car_size"]
                     + self.config["wheel_size"][1]) \
-                   * np.sin(curr_x[2]-np.pi/2.) + curr_x[1]
+            * np.sin(curr_x[2]-np.pi/2.) + curr_x[1]
 
         left_tire_x, left_tire_y = \
             square(center_x, center_y,
                    self.config["wheel_size"], curr_x[2])
 
         # right tire
-        center_x = (self.config["car_size"] \
+        center_x = (self.config["car_size"]
                     + self.config["wheel_size"][1]) \
-                   * np.cos(curr_x[2]+np.pi/2.) + curr_x[0]
+            * np.cos(curr_x[2]+np.pi/2.) + curr_x[0]
-        center_y = (self.config["car_size"] \
+        center_y = (self.config["car_size"]
                     + self.config["wheel_size"][1]) \
-                   * np.sin(curr_x[2]+np.pi/2.) + curr_x[1]
+            * np.sin(curr_x[2]+np.pi/2.) + curr_x[1]
 
         right_tire_x, right_tire_y = \
             square(center_x, center_y,
                    self.config["wheel_size"], curr_x[2])
 
         return car_x, car_y, car_angle_x, car_angle_y,\
-               left_tire_x, left_tire_y,\
+            left_tire_x, left_tire_y,\
-               right_tire_x, right_tire_y
+            right_tire_x, right_tire_y
+
 
 class TwoWheeledTrackEnv(Env):
     """ Two wheeled robot with constant goal Env
     """
+
     def __init__(self):
         """
         """
-        self.config = {"state_size" : 3,\
+        self.config = {"state_size": 3,
-                       "input_size" : 2,\
+                       "input_size": 2,
-                       "dt" : 0.01,\
+                       "dt": 0.01,
-                       "max_step" : 1000,\
+                       "max_step": 1000,
-                       "input_lower_bound": (-1.5, -3.14),\
+                       "input_lower_bound": (-1.5, -3.14),
-                       "input_upper_bound": (1.5, 3.14),\
+                       "input_upper_bound": (1.5, 3.14),
-                       "car_size": 0.2,\
+                       "car_size": 0.2,
                        "wheel_size": (0.075, 0.015)
                        }
 
@@ -220,12 +225,12 @@ class TwoWheeledTrackEnv(Env):
 
         # circle
         circle_1_x, circle_1_y = circle(linelength/2., circle_radius,
-            circle_radius, start=-np.pi/2., end=np.pi/2., n_point=50)
+                                        circle_radius, start=-np.pi/2., end=np.pi/2., n_point=50)
-        circle_1 = np.stack((circle_1_x , circle_1_y), axis=1)
+        circle_1 = np.stack((circle_1_x, circle_1_y), axis=1)
 
         circle_2_x, circle_2_y = circle(-linelength/2., circle_radius,
-            circle_radius, start=np.pi/2., end=3*np.pi/2., n_point=50)
+                                        circle_radius, start=np.pi/2., end=3*np.pi/2., n_point=50)
-        circle_2 = np.stack((circle_2_x , circle_2_y), axis=1)
+        circle_2 = np.stack((circle_2_x, circle_2_y), axis=1)
 
         road_pos = np.concatenate((line_1, circle_1, line_2, circle_2), axis=0)
 
@@ -293,8 +298,8 @@ class TwoWheeledTrackEnv(Env):
         self.step_count += 1
 
         return next_x.flatten(), costs, \
-               self.step_count > self.config["max_step"], \
+            self.step_count > self.config["max_step"], \
-               {"goal_state" : self.g_traj}
+            {"goal_state": self.g_traj}
 
     def plot_func(self, to_plot, i=None, history_x=None, history_g_x=None):
         """ plot cartpole object function
@@ -333,7 +338,7 @@ class TwoWheeledTrackEnv(Env):
         # set imgs
         # car imgs
         car_x, car_y, car_angle_x, car_angle_y, \
-        left_tire_x, left_tire_y, right_tire_x, right_tire_y = \
+            left_tire_x, left_tire_y, right_tire_x, right_tire_y = \
             self._plot_car(history_x[i])
 
         to_plot["car"].set_data(car_x, car_y)
@@ -354,29 +359,29 @@ class TwoWheeledTrackEnv(Env):
                               self.config["car_size"], curr_x[2])
 
         # left tire
-        center_x = (self.config["car_size"] \
+        center_x = (self.config["car_size"]
                     + self.config["wheel_size"][1]) \
-                   * np.cos(curr_x[2]-np.pi/2.) + curr_x[0]
+            * np.cos(curr_x[2]-np.pi/2.) + curr_x[0]
-        center_y = (self.config["car_size"] \
+        center_y = (self.config["car_size"]
                     + self.config["wheel_size"][1]) \
-                   * np.sin(curr_x[2]-np.pi/2.) + curr_x[1]
+            * np.sin(curr_x[2]-np.pi/2.) + curr_x[1]
 
         left_tire_x, left_tire_y = \
             square(center_x, center_y,
                    self.config["wheel_size"], curr_x[2])
 
         # right tire
-        center_x = (self.config["car_size"] \
+        center_x = (self.config["car_size"]
                     + self.config["wheel_size"][1]) \
-                   * np.cos(curr_x[2]+np.pi/2.) + curr_x[0]
+            * np.cos(curr_x[2]+np.pi/2.) + curr_x[0]
-        center_y = (self.config["car_size"] \
+        center_y = (self.config["car_size"]
                     + self.config["wheel_size"][1]) \
-                   * np.sin(curr_x[2]+np.pi/2.) + curr_x[1]
+            * np.sin(curr_x[2]+np.pi/2.) + curr_x[1]
 
         right_tire_x, right_tire_y = \
             square(center_x, center_y,
                    self.config["wheel_size"], curr_x[2])
 
         return car_x, car_y, car_angle_x, car_angle_y,\
-               left_tire_x, left_tire_y,\
+            left_tire_x, left_tire_y,\
-               right_tire_x, right_tire_y
+            right_tire_x, right_tire_y

PythonLinearNonlinearControl/helper.py

@@ -7,6 +7,7 @@ import six
 import pickle
 from logging import DEBUG, basicConfig, getLogger, FileHandler, StreamHandler, Formatter, Logger
 
+
 def make_logger(save_dir):
     """
     Args:
@@ -33,6 +34,7 @@ def make_logger(save_dir):
     # sh_handler = StreamHandler()
     # logger.addHandler(sh_handler)
 
+
 def int_tuple(s):
     """ transform str to tuple
     Args:
@@ -42,6 +44,7 @@ def int_tuple(s):
     """
     return tuple(int(i) for i in s.split(','))
 
+
 def bool_flag(s):
     """ transform str to bool flg
     Args:
@@ -54,6 +57,7 @@ def bool_flag(s):
     msg = 'Invalid value "%s" for bool flag (should be 0 or 1)'
     raise ValueError(msg % s)
 
+
 def file_exists(path):
     """ Check file existence on given path
     Args:
@@ -63,6 +67,7 @@ def file_exists(path):
     """
     return os.path.exists(path)
 
+
 def create_dir_if_not_exist(outdir):
     """ Check directory existence and creates new directory if not exist
     Args:  
@@ -77,6 +82,7 @@ def create_dir_if_not_exist(outdir):
             return
     os.makedirs(outdir)
 
+
 def write_text_to_file(file_path, data):
     """ Write given text data to file
     Args:
@@ -86,6 +92,7 @@ def write_text_to_file(file_path, data):
     with open(file_path, 'w') as f:
         f.write(data)
 
+
 def read_text_from_file(file_path):
     """ Read given file as text
     Args:
@@ -96,6 +103,7 @@ def read_text_from_file(file_path):
     with open(file_path, 'r') as f:
         return f.read()
 
+
 def save_pickle(file_path, data):
     """ pickle given data to file
     Args:
@@ -105,6 +113,7 @@ def save_pickle(file_path, data):
     with open(file_path, 'wb') as f:
         pickle.dump(data, f)
 
+
 def load_pickle(file_path):
     """ load pickled data from file
     Args:
@@ -118,6 +127,7 @@ def load_pickle(file_path):
         else:
             return pickle.load(f, encoding='bytes')
 
+
 def prepare_output_dir(base_dir, args, time_format='%Y-%m-%d-%H%M%S'):
     """ prepare a directory with current datetime as name.
     created directory contains the command and args when the script was called as text file.

PythonLinearNonlinearControl/models/cartpole.py

@@ -2,9 +2,11 @@ import numpy as np
 
 from .model import Model
 
+
 class CartPoleModel(Model):
     """ cartpole model
     """
+
     def __init__(self, config):
         """
         """
@@ -31,21 +33,21 @@ class CartPoleModel(Model):
             # x
             d_x0 = curr_x[1]
             # v_x
-            d_x1 = (u[0] + self.mp * np.sin(curr_x[2]) \
+            d_x1 = (u[0] + self.mp * np.sin(curr_x[2])
-                        * (self.l * (curr_x[3]**2) \
+                    * (self.l * (curr_x[3]**2)
-                           + self.g * np.cos(curr_x[2]))) \
+                       + self.g * np.cos(curr_x[2]))) \
-                   / (self.mc + self.mp * (np.sin(curr_x[2])**2))
+                / (self.mc + self.mp * (np.sin(curr_x[2])**2))
             # theta
             d_x2 = curr_x[3]
             # v_theta
-            d_x3 = (-u[0] * np.cos(curr_x[2]) \
+            d_x3 = (-u[0] * np.cos(curr_x[2])
-                    - self.mp * self.l * (curr_x[3]**2) \
+                    - self.mp * self.l * (curr_x[3]**2)
-                      * np.cos(curr_x[2]) * np.sin(curr_x[2]) \
+                    * np.cos(curr_x[2]) * np.sin(curr_x[2])
                     - (self.mc + self.mp) * self.g * np.sin(curr_x[2])) \
-                   / (self.l * (self.mc + self.mp * (np.sin(curr_x[2])**2)))
+                / (self.l * (self.mc + self.mp * (np.sin(curr_x[2])**2)))
 
             next_x = curr_x +\
-                     np.array([d_x0, d_x1, d_x2, d_x3]) * self.dt 
+                np.array([d_x0, d_x1, d_x2, d_x3]) * self.dt
 
             return next_x
 
@@ -53,21 +55,21 @@ class CartPoleModel(Model):
             # x
             d_x0 = curr_x[:, 1]
             # v_x
-            d_x1 = (u[:, 0] + self.mp * np.sin(curr_x[:, 2]) \
+            d_x1 = (u[:, 0] + self.mp * np.sin(curr_x[:, 2])
-                        * (self.l * (curr_x[:, 3]**2) \
+                    * (self.l * (curr_x[:, 3]**2)
-                           + self.g * np.cos(curr_x[:, 2]))) \
+                       + self.g * np.cos(curr_x[:, 2]))) \
-                   / (self.mc + self.mp * (np.sin(curr_x[:, 2])**2))
+                / (self.mc + self.mp * (np.sin(curr_x[:, 2])**2))
             # theta
             d_x2 = curr_x[:, 3]
             # v_theta
-            d_x3 = (-u[:, 0] * np.cos(curr_x[:, 2]) \
+            d_x3 = (-u[:, 0] * np.cos(curr_x[:, 2])
-                    - self.mp * self.l * (curr_x[:, 3]**2) \
+                    - self.mp * self.l * (curr_x[:, 3]**2)
-                      * np.cos(curr_x[:, 2]) * np.sin(curr_x[:, 2]) \
+                    * np.cos(curr_x[:, 2]) * np.sin(curr_x[:, 2])
                     - (self.mc + self.mp) * self.g * np.sin(curr_x[:, 2])) \
-                   / (self.l * (self.mc + self.mp * (np.sin(curr_x[:, 2])**2)))
+                / (self.l * (self.mc + self.mp * (np.sin(curr_x[:, 2])**2)))
 
             next_x = curr_x +\
-                     np.stack((d_x0, d_x1, d_x2, d_x3), axis=1) * self.dt 
+                np.stack((d_x0, d_x1, d_x2, d_x3), axis=1) * self.dt
 
             return next_x
 
@@ -95,36 +97,36 @@ class CartPoleModel(Model):
 
         # f_theta
         tmp = ((self.mc + self.mp * np.sin(xs[:, 2])**2)**(-2)) \
-              * self.mp * 2. * np.sin(xs[:, 2]) * np.cos(xs[:, 2])
+            * self.mp * 2. * np.sin(xs[:, 2]) * np.cos(xs[:, 2])
         tmp2 = 1. / (self.mc + self.mp * (np.sin(xs[:, 2])**2))
 
         f_x[:, 1, 2] = - us[:, 0] * tmp \
-                       - tmp * (self.mp * np.sin(xs[:, 2]) \
+                       - tmp * (self.mp * np.sin(xs[:, 2])
-                                * (self.l * xs[:, 3]**2 \
+                                * (self.l * xs[:, 3]**2
                                    + self.g * np.cos(xs[:, 2]))) \
-                       + tmp2 * (self.mp * np.cos(xs[:, 2]) * self.l \
+            + tmp2 * (self.mp * np.cos(xs[:, 2]) * self.l
-                                 * xs[:, 3]**2 \
+                      * xs[:, 3]**2
-                                 + self.mp * self.g * (np.cos(xs[:, 2])**2 \
+                      + self.mp * self.g * (np.cos(xs[:, 2])**2
-                                                       - np.sin(xs[:, 2])**2))
+                                            - np.sin(xs[:, 2])**2))
         f_x[:, 3, 2] = - 1. / self.l * tmp \
-                        * (-us[:, 0] * np.cos(xs[:, 2]) \
+            * (-us[:, 0] * np.cos(xs[:, 2])
-                           - self.mp * self.l * (xs[:, 3]**2) \
+               - self.mp * self.l * (xs[:, 3]**2)
-                           * np.cos(xs[:, 2]) * np.sin(xs[:, 2]) \
+               * np.cos(xs[:, 2]) * np.sin(xs[:, 2])
-                           - (self.mc + self.mp) * self.g * np.sin(xs[:, 2])) \
+               - (self.mc + self.mp) * self.g * np.sin(xs[:, 2])) \
-                        +  1. / self.l * tmp2 \
+            + 1. / self.l * tmp2 \
-                           * (us[:, 0] * np.sin(xs[:, 2]) \
+            * (us[:, 0] * np.sin(xs[:, 2])
-                              - self.mp * self.l * xs[:, 3]**2 \
+               - self.mp * self.l * xs[:, 3]**2
-                                * (np.cos(xs[:, 2])**2 - np.sin(xs[:, 2])**2) \
+               * (np.cos(xs[:, 2])**2 - np.sin(xs[:, 2])**2)
-                              - (self.mc + self.mp) \
+               - (self.mc + self.mp)
-                                 * self.g * np.cos(xs[:, 2]))
+               * self.g * np.cos(xs[:, 2]))
 
         # f_theta_dot
-        f_x[:, 1, 3] = tmp2 * (self.mp * np.sin(xs[:, 2]) \
+        f_x[:, 1, 3] = tmp2 * (self.mp * np.sin(xs[:, 2])
-                       * self.l * 2 * xs[:, 3])
+                               * self.l * 2 * xs[:, 3])
         f_x[:, 2, 3] = np.ones(pred_len)
         f_x[:, 3, 3] = 1. / self.l * tmp2 \
-                        * (-2. * self.mp * self.l * xs[:, 3] \
+            * (-2. * self.mp * self.l * xs[:, 3]
-                           * np.cos(xs[:, 2]) * np.sin(xs[:, 2]))
+               * np.cos(xs[:, 2]) * np.sin(xs[:, 2]))
 
         return f_x * dt + np.eye(state_size)  # to discrete form
 
@@ -150,8 +152,8 @@ class CartPoleModel(Model):
         f_u[:, 1, 0] = 1. / (self.mc + self.mp * (np.sin(xs[:, 2])**2))
 
         f_u[:, 3, 0] = -np.cos(xs[:, 2]) \
-                       / (self.l * (self.mc \
+            / (self.l * (self.mc
-                                    + self.mp * (np.sin(xs[:, 2])**2)))
+                         + self.mp * (np.sin(xs[:, 2])**2)))
 
         return f_u * dt  # to discrete form
 

PythonLinearNonlinearControl/models/first_order_lag.py

@@ -3,6 +3,7 @@ import scipy.linalg
 from scipy import integrate
 from .model import LinearModel
 
+
 class FirstOrderLagModel(LinearModel):
     """ first order lag model
     Attributes:
@@ -10,13 +11,15 @@ class FirstOrderLagModel(LinearModel):
         u (numpy.ndarray):
         history_pred_xs (numpy.ndarray):
     """
+
     def __init__(self, config, tau=0.63):
         """
         Args:
             tau (float): time constant 
         """
         # param
-        self.A, self.B = self._to_state_space(tau, dt=config.DT)  # discrete system
+        self.A, self.B = self._to_state_space(
+            tau, dt=config.DT)  # discrete system
         super(FirstOrderLagModel, self).__init__(self.A, self.B)
 
     @staticmethod
@@ -31,20 +34,21 @@ class FirstOrderLagModel(LinearModel):
         """
         # continuous
         Ac = np.array([[-1./tau, 0., 0., 0.],
-                      [0., -1./tau, 0., 0.],
+                       [0., -1./tau, 0., 0.],
-                      [1., 0., 0., 0.], 
+                       [1., 0., 0., 0.],
-                      [0., 1., 0., 0.]])
+                       [0., 1., 0., 0.]])
         Bc = np.array([[1./tau, 0.],
-                      [0., 1./tau],
+                       [0., 1./tau],
-                      [0., 0.],
+                       [0., 0.],
-                      [0., 0.]])
+                       [0., 0.]])
         # to discrete system
         A = scipy.linalg.expm(dt*Ac)
         # B = np.matmul(np.matmul(scipy.linalg.expm(Ac*dt)-scipy.linalg.expm(Ac*0.), np.linalg.inv(Ac)), Bc)
         B = np.zeros_like(Bc)
         for m in range(Bc.shape[0]):
             for n in range(Bc.shape[1]):
-                integrate_fn = lambda tau: np.matmul(scipy.linalg.expm(Ac*tau), Bc)[m, n]
+                def integrate_fn(tau): return np.matmul(
+                    scipy.linalg.expm(Ac*tau), Bc)[m, n]
                 sol = integrate.quad(integrate_fn, 0, dt)
                 B[m, n] = sol[0]
 

PythonLinearNonlinearControl/models/make_models.py

@@ -1,6 +1,8 @@
 from .first_order_lag import FirstOrderLagModel
 from .two_wheeled import TwoWheeledModel
 from .cartpole import CartPoleModel
+from .nonlinear_sample_system import NonlinearSampleSystemModel
+
 
 def make_model(args, config):
 
@@ -10,5 +12,7 @@ def make_model(args, config):
         return TwoWheeledModel(config)
     elif args.env == "CartPole":
         return CartPoleModel(config)
+    elif args.env == "NonlinearSample":
+        return NonlinearSampleSystemModel(config)
 
     raise NotImplementedError("There is not {} Model".format(args.env))

PythonLinearNonlinearControl/models/model.py

@@ -1,8 +1,10 @@
 import numpy as np
 
+
 class Model():
     """ base class of model
     """
+
     def __init__(self):
         """
         """
@@ -22,7 +24,7 @@ class Model():
                 or shape(pop_size, pred_len+1, state_size)
         """
         if len(us.shape) == 3:
-            pred_xs =self._predict_traj_alltogether(curr_x, us)
+            pred_xs = self._predict_traj_alltogether(curr_x, us)
         elif len(us.shape) == 2:
             pred_xs = self._predict_traj(curr_x, us)
         else:
@@ -75,8 +77,8 @@ class Model():
             # next_x.shape = (pop_size, state_size)
             next_x = self.predict_next_state(x, us[t])
             # update
-            pred_xs = np.concatenate((pred_xs, next_x[np.newaxis, :, :]),\
+            pred_xs = np.concatenate((pred_xs, next_x[np.newaxis, :, :]),
-                                      axis=0)
+                                     axis=0)
             x = next_x
 
         return np.transpose(pred_xs, (1, 0, 2))
@@ -99,13 +101,13 @@ class Model():
         # get size
         (pred_len, input_size) = us.shape
         # pred final adjoint state
-        lam = self.predict_terminal_adjoint_state(xs[-1],\
+        lam = self.predict_terminal_adjoint_state(xs[-1],
                                                   terminal_g_x=g_xs[-1])
         lams = lam[np.newaxis, :]
 
         for t in range(pred_len-1, 0, -1):
             prev_lam = \
-                self.predict_adjoint_state(lam, xs[t], us[t],\
+                self.predict_adjoint_state(lam, xs[t], us[t],
                                            goal=g_xs[t], t=t)
             # update
             lams = np.concatenate((prev_lam[np.newaxis, :], lams), axis=0)
@@ -175,6 +177,7 @@ class Model():
         raise NotImplementedError("Implement hessian of model \
                                    with respect to the input")
 
+
 class LinearModel(Model):
     """ discrete linear model, x[k+1] = Ax[k] + Bu[k]
 
@@ -182,6 +185,7 @@ class LinearModel(Model):
         A (numpy.ndarray): shape(state_size, state_size)
         B (numpy.ndarray): shape(state_size, input_size)
     """
+
     def __init__(self, A, B):
         """
         """
@@ -203,7 +207,7 @@ class LinearModel(Model):
         """
         if len(u.shape) == 1:
             next_x = np.matmul(self.A, curr_x[:, np.newaxis]) \
-                    + np.matmul(self.B, u[:, np.newaxis])
+                + np.matmul(self.B, u[:, np.newaxis])
 
             return next_x.flatten()
 

PythonLinearNonlinearControl/models/nonlinear_sample_system.py

@@ -0,0 +1,164 @@
+import numpy as np
+
+from .model import Model
+from ..common.utils import update_state_with_Runge_Kutta
+
+
+class NonlinearSampleSystemModel(Model):
+    """ nonlinear sample system model
+    """
+
+    def __init__(self, config):
+        """
+        """
+        super(NonlinearSampleSystemModel, self).__init__()
+        self.dt = config.DT
+
+    def predict_next_state(self, curr_x, u):
+        """ predict next state
+
+        Args:
+            curr_x (numpy.ndarray): current state, shape(state_size, ) or
+                shape(pop_size, state_size)
+            u (numpy.ndarray): input, shape(input_size, ) or
+                shape(pop_size, input_size)
+        Returns:
+            next_x (numpy.ndarray): next state, shape(state_size, ) or
+                shape(pop_size, state_size)
+        """
+        if len(u.shape) == 1:
+            func_1 = self._func_x_1
+            func_2 = self._func_x_2
+            functions = [func_1, func_2]
+            next_x = update_state_with_Runge_Kutta(
+                curr_x, u, functions, batch=False)
+            return next_x
+
+        elif len(u.shape) == 2:
+            def func_1(xs, us): return self._func_x_1(xs, us, batch=True)
+            def func_2(xs, us): return self._func_x_2(xs, us, batch=True)
+            functions = [func_1, func_2]
+            next_x = update_state_with_Runge_Kutta(
+                curr_x, u, functions, batch=True)
+
+            return next_x
+
+    def _func_x_1(self, x, u, batch=False):
+        if not batch:
+            x_dot = x[1]
+        else:
+            x_dot = x[:, 1]
+        return x_dot
+
+    def _func_x_2(self, x, u, batch=False):
+        if not batch:
+            x_dot = (1. - x[0]**2 - x[1]**2) * x[1] - x[0] + u[0]
+        else:
+            x_dot = (1. - x[:, 0]**2 - x[:, 1]**2) * \
+                x[:, 1] - x[:, 0] + u[:, 0]
+        return x_dot
+
+    def calc_f_x(self, xs, us, dt):
+        """ gradient of model with respect to the state in batch form
+        Args:
+            xs (numpy.ndarray): state, shape(pred_len+1, state_size)
+            us (numpy.ndarray): input, shape(pred_len, input_size,)
+
+        Return:
+            f_x (numpy.ndarray): gradient of model with respect to x,
+                shape(pred_len, state_size, state_size)
+
+        Notes:
+            This should be discrete form !!
+        """
+        # get size
+        (_, state_size) = xs.shape
+        (pred_len, _) = us.shape
+
+        f_x = np.zeros((pred_len, state_size, state_size))
+        f_x[:, 0, 1] = 1.
+        f_x[:, 1, 0] = 2. * xs[:, 0] * xs[:, 1] - 1.
+        f_x[:, 1, 1] = - 2. * xs[:, 1] * xs[:, 1] + \
+            (1. - xs[:, 0]**2 - xs[:, 1]**2)
+
+        return f_x * dt + np.eye(state_size)  # to discrete form
+
+    def calc_f_u(self, xs, us, dt):
+        """ gradient of model with respect to the input in batch form
+        Args:
+            xs (numpy.ndarray): state, shape(pred_len+1, state_size)
+            us (numpy.ndarray): input, shape(pred_len, input_size,)
+
+        Return:
+            f_u (numpy.ndarray): gradient of model with respect to x,
+                shape(pred_len, state_size, input_size)
+
+        Notes:
+            This should be discrete form !!
+        """
+        # get size
+        (_, state_size) = xs.shape
+        (pred_len, input_size) = us.shape
+
+        f_u = np.zeros((pred_len, state_size, input_size))
+
+        f_u[:, 1, 0] = 1.
+
+        return f_u * dt  # to discrete form
+
+    def calc_f_xx(self, xs, us, dt):
+        """ hessian of model with respect to the state in batch form
+
+        Args:
+            xs (numpy.ndarray): state, shape(pred_len+1, state_size)
+            us (numpy.ndarray): input, shape(pred_len, input_size,)
+
+        Return:
+            f_xx (numpy.ndarray): gradient of model with respect to x,
+                shape(pred_len, state_size, state_size, state_size)
+        """
+        # get size
+        (_, state_size) = xs.shape
+        (pred_len, _) = us.shape
+
+        f_xx = np.zeros((pred_len, state_size, state_size, state_size))
+
+        raise NotImplementedError
+
+    def calc_f_ux(self, xs, us, dt):
+        """ hessian of model with respect to state and input in batch form
+
+        Args:
+            xs (numpy.ndarray): state, shape(pred_len+1, state_size)
+            us (numpy.ndarray): input, shape(pred_len, input_size,)
+
+        Return:
+            f_ux (numpy.ndarray): gradient of model with respect to x,
+                shape(pred_len, state_size, input_size, state_size)
+        """
+        # get size
+        (_, state_size) = xs.shape
+        (pred_len, input_size) = us.shape
+
+        f_ux = np.zeros((pred_len, state_size, input_size, state_size))
+
+        raise NotImplementedError
+
+    def calc_f_uu(self, xs, us, dt):
+        """ hessian of model with respect to input in batch form
+
+        Args:
+            xs (numpy.ndarray): state, shape(pred_len+1, state_size)
+            us (numpy.ndarray): input, shape(pred_len, input_size,)
+
+        Return:
+            f_uu (numpy.ndarray): gradient of model with respect to x,
+                shape(pred_len, state_size, input_size, input_size)
+        """
+        # get size
+        (_, state_size) = xs.shape
+        (pred_len, input_size) = us.shape
+
+        f_uu = np.zeros((pred_len, state_size, input_size, input_size))
+
+        raise NotImplementedError

PythonLinearNonlinearControl/models/two_wheeled.py

@@ -2,9 +2,11 @@ import numpy as np
 
 from .model import Model
 
+
 class TwoWheeledModel(Model):
     """ two wheeled model
     """
+
     def __init__(self, config):
         """
         """

PythonLinearNonlinearControl/planners/closest_point_planner.py

@@ -1,9 +1,11 @@
 import numpy as np
 from .planner import Planner
 
+
 class ClosestPointPlanner(Planner):
     """ This planner make goal state according to goal path
     """
+
     def __init__(self, config):
         """
         """

PythonLinearNonlinearControl/planners/const_planner.py

@@ -1,9 +1,11 @@
 import numpy as np
 from .planner import Planner
 
+
 class ConstantPlanner(Planner):
     """ This planner make constant goal state
     """
+
     def __init__(self, config):
         """
         """

PythonLinearNonlinearControl/planners/make_planners.py

@@ -1,6 +1,7 @@
 from .const_planner import ConstantPlanner
 from .closest_point_planner import ClosestPointPlanner
 
+
 def make_planner(args, config):
 
     if args.env == "FirstOrderLag":
@@ -11,5 +12,8 @@ def make_planner(args, config):
         return ClosestPointPlanner(config)
     elif args.env == "CartPole":
         return ConstantPlanner(config)
+    elif args.env == "NonlinearSample":
+        return ConstantPlanner(config)
 
-    raise NotImplementedError("There is not {} Planner".format(args.planner_type))
+    raise NotImplementedError(
+        "There is not {} Planner".format(args.planner_type))

PythonLinearNonlinearControl/planners/planner.py

@@ -1,8 +1,10 @@
 import numpy as np
 
+
 class Planner():
     """
     """
+
     def __init__(self):
         """
         """

PythonLinearNonlinearControl/plotters/animator.py

@@ -8,9 +8,11 @@ import matplotlib.animation as animation
 
 logger = getLogger(__name__)
 
+
 class Animator():
     """ animation class
     """
+
     def __init__(self, env, args=None):
         """
         """
@@ -65,7 +67,7 @@ class Animator():
         """
         # set up animation figures
         self._setup()
-        _update_img = lambda i: self._update_img(i, history_x, history_g_x)
+        def _update_img(i): return self._update_img(i, history_x, history_g_x)
 
         # Set up formatting for the movie files
         Writer = animation.writers['ffmpeg']

PythonLinearNonlinearControl/plotters/plot_func.py

@@ -5,6 +5,7 @@ import matplotlib.pyplot as plt
 
 from ..helper import save_pickle, load_pickle
 
+
 def plot_result(history, history_g=None, ylabel="x",
                 save_dir="./result", name="state_history"):
     """
@@ -28,7 +29,7 @@ def plot_result(history, history_g=None, ylabel="x",
         def plot(axis, history, history_g=None):
             axis.plot(range(iters), history, c="r", linewidth=3)
             if history_g is not None:
-                axis.plot(range(iters), history_g,\
+                axis.plot(range(iters), history_g,
                           c="b", linewidth=3, label="goal")
 
         if i < size:
@@ -47,6 +48,7 @@ def plot_result(history, history_g=None, ylabel="x",
         axis1.legend(ncol=1, bbox_to_anchor=(0., 1.02, 1., 0.102), loc=3)
         figure.savefig(path, bbox_inches="tight", pad_inches=0.05)
 
+
 def plot_results(history_x, history_u, history_g=None, args=None):
     """
 
@@ -64,12 +66,13 @@ def plot_results(history_x, history_u, history_g=None, args=None):
         controller_type = args.controller_type
 
     plot_result(history_x, history_g=history_g, ylabel="x",
-                name= env + "-state_history",
+                name=env + "-state_history",
                 save_dir="./result/" + controller_type)
     plot_result(history_u, history_g=np.zeros_like(history_u), ylabel="u",
-                name= env + "-input_history",
+                name=env + "-input_history",
                 save_dir="./result/" + controller_type)
 
+
 def save_plot_data(history_x, history_u, history_g=None, args=None):
     """ save plot data
 
@@ -98,6 +101,7 @@ def save_plot_data(history_x, history_u, history_g=None, args=None):
                         env + "-history_g.pkl")
     save_pickle(path, history_g)
 
+
 def load_plot_data(env, controller_type, result_dir="./result"):
     """
     Args:
@@ -123,6 +127,7 @@ def load_plot_data(env, controller_type, result_dir="./result"):
 
     return history_x, history_u, history_g
 
+
 def plot_multi_result(histories, histories_g=None, labels=None, ylabel="x",
                       save_dir="./result", name="state_history"):
     """
@@ -146,17 +151,17 @@ def plot_multi_result(histories, histories_g=None, labels=None, ylabel="x",
             axis.plot(range(iters), history,
                       linewidth=3, label=label, alpha=0.7, linestyle="dashed")
             if history_g is not None:
-                axis.plot(range(iters), history_g,\
+                axis.plot(range(iters), history_g,
                           c="b", linewidth=3)
 
         if i < size:
             for j, (history, history_g) \
-                in enumerate(zip(histories, histories_g)):
+                    in enumerate(zip(histories, histories_g)):
                 plot(axis1, history[:, i],
                      history_g=history_g[:, i], label=labels[j])
         if i+1 < size:
             for j, (history, history_g) in \
-                enumerate(zip(histories, histories_g)):
+                    enumerate(zip(histories, histories_g)):
                 plot(axis2, history[:, i+1],
                      history_g=history_g[:, i+1], label=labels[j])
 

PythonLinearNonlinearControl/plotters/plot_objs.py

@@ -5,6 +5,7 @@ import matplotlib.pyplot as plt
 
 from ..common.utils import rotate_pos
 
+
 def circle(center_x, center_y, radius, start=0., end=2*np.pi, n_point=100):
     """ Create circle matrix
 
@@ -29,6 +30,7 @@ def circle(center_x, center_y, radius, start=0., end=2*np.pi, n_point=100):
 
     return np.array(circle_xs), np.array(circle_ys)
 
+
 def circle_with_angle(center_x, center_y, radius, angle):
     """ Create circle matrix with angle line matrix
 
@@ -50,6 +52,7 @@ def circle_with_angle(center_x, center_y, radius, angle):
 
     return circle_x, circle_y, angle_x, angle_y
 
+
 def square(center_x, center_y, shape, angle):
     """ Create square
 
@@ -77,6 +80,7 @@ def square(center_x, center_y, shape, angle):
 
     return trans_points[:, 0], trans_points[:, 1]
 
+
 def square_with_angle(center_x, center_y, shape, angle):
     """ Create square with angle line
 

PythonLinearNonlinearControl/runners/make_runners.py

@@ -1,4 +1,5 @@
 from .runner import ExpRunner
 
+
 def make_runner(args):
     return ExpRunner()

PythonLinearNonlinearControl/runners/runner.py

@@ -4,9 +4,11 @@ import numpy as np
 
 logger = getLogger(__name__)
 
+
 class ExpRunner():
     """ experiment runner
     """
+
     def __init__(self):
         """
         """
@@ -46,6 +48,6 @@ class ExpRunner():
             score += cost
             step_count += 1
 
-        logger.debug("Controller type = {}, Score = {}"\
+        logger.debug("Controller type = {}, Score = {}"
                      .format(controller, score))
         return np.array(history_x), np.array(history_u), np.array(history_g)

README.md

@@ -71,6 +71,7 @@ There are 4 example environments, "FirstOrderLag", "TwoWheeledConst", "TwoWheele
 | Two wheeled System (Constant Goal) | x | ✓ | 3 | 2 | 
 | Two wheeled System (Moving Goal) | x | ✓ | 3 | 2 | 
 | Cartpole (Swing up) | x | ✓ | 4 | 1 | 
+| Nonlinear Sample System Env | x | ✓ | 2 | 1 | 
 
 All states and inputs of environments are continuous.
 **It should be noted that the algorithms for linear model could be applied to nonlinear enviroments if you have linealized the model of nonlinear environments.**

scripts/show_result.py

@@ -6,7 +6,8 @@ import numpy as np
 import matplotlib.pyplot as plt
 
 from PythonLinearNonlinearControl.plotters.plot_func import load_plot_data, \
-                                                            plot_multi_result
+    plot_multi_result
+
 
 def run(args):
 
@@ -39,7 +40,8 @@ def run(args):
                       ylabel="x")
 
     plot_multi_result(history_us, histories_g=np.zeros_like(history_us),
-                     labels=controllers, ylabel="u", name="input_history")
+                      labels=controllers, ylabel="u", name="input_history")
+
 
 def main():
     parser = argparse.ArgumentParser()
@@ -51,5 +53,6 @@ def main():
 
     run(args)
 
+
 if __name__ == "__main__":
     main()

scripts/simple_run.py

@@ -8,9 +8,10 @@ from PythonLinearNonlinearControl.models.make_models import make_model
 from PythonLinearNonlinearControl.envs.make_envs import make_env
 from PythonLinearNonlinearControl.runners.make_runners import make_runner
 from PythonLinearNonlinearControl.plotters.plot_func import plot_results, \
-                                                            save_plot_data
+    save_plot_data
 from PythonLinearNonlinearControl.plotters.animator import Animator
 
+
 def run(args):
     # logger
     make_logger(args.result_dir)
@@ -44,17 +45,19 @@ def run(args):
         animator = Animator(env, args=args)
         animator.draw(history_x, history_g)
 
+
 def main():
     parser = argparse.ArgumentParser()
 
-    parser.add_argument("--controller_type", type=str, default="CEM")
+    parser.add_argument("--controller_type", type=str, default="DDP")
-    parser.add_argument("--env", type=str, default="TwoWheeledTrack")
+    parser.add_argument("--env", type=str, default="NonlinearSample")
-    parser.add_argument("--save_anim", type=bool_flag, default=1)
+    parser.add_argument("--save_anim", type=bool_flag, default=0)
     parser.add_argument("--result_dir", type=str, default="./result")
 
     args = parser.parse_args()
 
     run(args)
 
+
 if __name__ == "__main__":
     main()