214 lines
6.9 KiB
Python
214 lines
6.9 KiB
Python
from typing import Iterable, Union
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import matplotlib.pyplot as plt
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import numpy as np
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from gym import spaces
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from gym.utils import seeding
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from mp_env_api.envs.mp_env import MpEnv
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from mp_env_api.envs.mp_env_wrapper import MPEnvWrapper
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class SimpleReacherEnv(MpEnv):
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"""
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Simple Reaching Task without any physics simulation.
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Returns no reward until 150 time steps. This allows the agent to explore the space, but requires precise actions
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towards the end of the trajectory.
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"""
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def __init__(self, n_links: int, target: Union[None, Iterable] = None, random_start: bool = True):
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super().__init__()
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self.link_lengths = np.ones(n_links)
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self.n_links = n_links
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self._dt = 0.1
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self.random_start = random_start
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self._joints = None
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self._joint_angles = None
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self._angle_velocity = None
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self._start_pos = np.zeros(self.n_links)
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self._start_vel = np.zeros(self.n_links)
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self._target = target # provided target value
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self._goal = None # updated goal value, does not change when target != None
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self.max_torque = 1
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self.steps_before_reward = 199
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action_bound = np.ones((self.n_links,)) * self.max_torque
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state_bound = np.hstack([
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[np.pi] * self.n_links, # cos
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[np.pi] * self.n_links, # sin
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[np.inf] * self.n_links, # velocity
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[np.inf] * 2, # x-y coordinates of target distance
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[np.inf] # env steps, because reward start after n steps TODO: Maybe
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])
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self.action_space = spaces.Box(low=-action_bound, high=action_bound, shape=action_bound.shape)
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self.observation_space = spaces.Box(low=-state_bound, high=state_bound, shape=state_bound.shape)
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# containers for plotting
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self.metadata = {'render.modes': ["human"]}
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self.fig = None
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self._steps = 0
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self.seed()
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@property
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def dt(self) -> Union[float, int]:
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return self._dt
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def step(self, action: np.ndarray):
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"""
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A single step with action in torque space
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"""
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# action = self._add_action_noise(action)
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ac = np.clip(action, -self.max_torque, self.max_torque)
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self._angle_velocity = self._angle_velocity + self.dt * ac
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self._joint_angles = self._joint_angles + self.dt * self._angle_velocity
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self._update_joints()
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reward, info = self._get_reward(action)
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self._steps += 1
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done = False
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return self._get_obs().copy(), reward, done, info
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def reset(self):
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# TODO: maybe do initialisation more random?
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# Sample only orientation of first link, i.e. the arm is always straight.
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if self.random_start:
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self._joint_angles = np.hstack([[self.np_random.uniform(-np.pi, np.pi)], np.zeros(self.n_links - 1)])
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self._start_pos = self._joint_angles.copy()
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else:
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self._joint_angles = self._start_pos
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self._generate_goal()
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self._angle_velocity = self._start_vel
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self._joints = np.zeros((self.n_links + 1, 2))
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self._update_joints()
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self._steps = 0
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return self._get_obs().copy()
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def _update_joints(self):
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"""
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update joints to get new end-effector position. The other links are only required for rendering.
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Returns:
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"""
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angles = np.cumsum(self._joint_angles)
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x = self.link_lengths * np.vstack([np.cos(angles), np.sin(angles)])
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self._joints[1:] = self._joints[0] + np.cumsum(x.T, axis=0)
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def _get_reward(self, action: np.ndarray):
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diff = self.end_effector - self._goal
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reward_dist = 0
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if self._steps >= self.steps_before_reward:
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reward_dist -= np.linalg.norm(diff)
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# reward_dist = np.exp(-0.1 * diff ** 2).mean()
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# reward_dist = - (diff ** 2).mean()
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reward_ctrl = (action ** 2).sum()
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reward = reward_dist - reward_ctrl
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return reward, dict(reward_dist=reward_dist, reward_ctrl=reward_ctrl)
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def _get_obs(self):
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theta = self._joint_angles
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return np.hstack([
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np.cos(theta),
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np.sin(theta),
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self._angle_velocity,
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self.end_effector - self._goal,
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self._steps
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])
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def _generate_goal(self):
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if self._target is None:
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total_length = np.sum(self.link_lengths)
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goal = np.array([total_length, total_length])
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while np.linalg.norm(goal) >= total_length:
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goal = self.np_random.uniform(low=-total_length, high=total_length, size=2)
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else:
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goal = np.copy(self._target)
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self._goal = goal
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def render(self, mode='human'): # pragma: no cover
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if self.fig is None:
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# Create base figure once on the beginning. Afterwards only update
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plt.ion()
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self.fig = plt.figure()
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ax = self.fig.add_subplot(1, 1, 1)
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# limits
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lim = np.sum(self.link_lengths) + 0.5
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ax.set_xlim([-lim, lim])
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ax.set_ylim([-lim, lim])
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self.line, = ax.plot(self._joints[:, 0], self._joints[:, 1], 'ro-', markerfacecolor='k')
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goal_pos = self._goal.T
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self.goal_point, = ax.plot(goal_pos[0], goal_pos[1], 'gx')
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self.goal_dist, = ax.plot([self.end_effector[0], goal_pos[0]], [self.end_effector[1], goal_pos[1]], 'g--')
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self.fig.show()
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self.fig.gca().set_title(f"Iteration: {self._steps}, distance: {self.end_effector - self._goal}")
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# goal
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goal_pos = self._goal.T
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if self._steps == 1:
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self.goal_point.set_data(goal_pos[0], goal_pos[1])
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# arm
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self.line.set_data(self._joints[:, 0], self._joints[:, 1])
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# distance between end effector and goal
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self.goal_dist.set_data([self.end_effector[0], goal_pos[0]], [self.end_effector[1], goal_pos[1]])
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self.fig.canvas.draw()
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self.fig.canvas.flush_events()
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def seed(self, seed=None):
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self.np_random, seed = seeding.np_random(seed)
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return [seed]
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def close(self):
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del self.fig
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@property
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def end_effector(self):
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return self._joints[self.n_links].T
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class SimpleReacherMPWrapper(MPEnvWrapper):
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@property
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def active_obs(self):
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return np.hstack([
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[self.env.random_start] * self.env.n_links, # cos
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[self.env.random_start] * self.env.n_links, # sin
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[self.env.random_start] * self.env.n_links, # velocity
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[True] * 2, # x-y coordinates of target distance
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[False] # env steps
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])
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@property
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def start_pos(self):
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return self._start_pos
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@property
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def goal_pos(self):
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raise ValueError("Goal position is not available and has to be learnt based on the environment.")
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@property
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def dt(self) -> Union[float, int]:
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return self.env.dt
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