193 lines
8.6 KiB
Python
193 lines
8.6 KiB
Python
from abc import ABC
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from typing import Tuple, Union
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import gym
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import numpy as np
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from gym import spaces
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from mp_pytorch.mp.mp_interfaces import MPInterface
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from alr_envs.black_box.controller.base_controller import BaseController
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from alr_envs.black_box.raw_interface_wrapper import RawInterfaceWrapper
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from alr_envs.utils.utils import get_numpy
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class BlackBoxWrapper(gym.ObservationWrapper):
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def __init__(self,
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env: RawInterfaceWrapper,
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trajectory_generator: MPInterface,
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tracking_controller: BaseController,
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duration: float,
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verbose: int = 1,
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learn_sub_trajectories: bool = False,
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replanning_schedule: Union[None, callable] = None,
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reward_aggregation: callable = np.sum
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):
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"""
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gym.Wrapper for leveraging a black box approach with a trajectory generator.
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Args:
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env: The (wrapped) environment this wrapper is applied on
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trajectory_generator: Generates the full or partial trajectory
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tracking_controller: Translates the desired trajectory to raw action sequences
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duration: Length of the trajectory of the movement primitive in seconds
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verbose: level of detail for returned values in info dict.
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learn_sub_trajectories: Transforms full episode learning into learning sub-trajectories, similar to
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step-based learning
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replanning_schedule: callable that receives
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reward_aggregation: function that takes the np.ndarray of step rewards as input and returns the trajectory
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reward, default summation over all values.
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"""
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super().__init__(env)
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self.duration = duration
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self.learn_sub_trajectories = learn_sub_trajectories
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self.replanning_schedule = replanning_schedule
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self.current_traj_steps = 0
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# trajectory generation
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self.traj_gen = trajectory_generator
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self.tracking_controller = tracking_controller
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# self.time_steps = np.linspace(0, self.duration, self.traj_steps)
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# self.traj_gen.set_mp_times(self.time_steps)
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self.traj_gen.set_duration(np.array([self.duration]), np.array([self.dt]))
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# reward computation
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self.reward_aggregation = reward_aggregation
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# spaces
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self.return_context_observation = not (self.learn_sub_trajectories or replanning_schedule)
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self.traj_gen_action_space = self.get_traj_gen_action_space()
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self.action_space = self.get_action_space()
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self.observation_space = spaces.Box(low=self.env.observation_space.low[self.env.context_mask],
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high=self.env.observation_space.high[self.env.context_mask],
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dtype=self.env.observation_space.dtype)
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# rendering
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self.render_kwargs = {}
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self.verbose = verbose
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def observation(self, observation):
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# return context space if we are
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return observation[self.env.context_mask] if self.return_context_observation else observation
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def get_trajectory(self, action: np.ndarray) -> Tuple:
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clipped_params = np.clip(action, self.traj_gen_action_space.low, self.traj_gen_action_space.high)
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self.traj_gen.set_params(clipped_params)
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# TODO: Bruce said DMP, ProMP, ProDMP can have 0 bc_time for sequencing
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# TODO Check with Bruce for replanning
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self.traj_gen.set_boundary_conditions(
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bc_time=np.zeros((1,)) if not self.replanning_schedule else self.current_traj_steps * self.dt,
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bc_pos=self.current_pos, bc_vel=self.current_vel)
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# TODO: is this correct for replanning? Do we need to adjust anything here?
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self.traj_gen.set_duration(None if self.learn_sub_trajectories else self.duration, np.array([self.dt]))
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traj_dict = self.traj_gen.get_trajs(get_pos=True, get_vel=True)
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trajectory_tensor, velocity_tensor = traj_dict['pos'], traj_dict['vel']
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return get_numpy(trajectory_tensor), get_numpy(velocity_tensor)
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def get_traj_gen_action_space(self):
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"""This function can be used to set up an individual space for the parameters of the traj_gen."""
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min_action_bounds, max_action_bounds = self.traj_gen.get_param_bounds()
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mp_action_space = gym.spaces.Box(low=min_action_bounds.numpy(), high=max_action_bounds.numpy(),
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dtype=np.float32)
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return mp_action_space
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def get_action_space(self):
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"""
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This function can be used to modify the action space for considering actions which are not learned via motion
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primitives. E.g. ball releasing time for the beer pong task. By default, it is the parameter space of the
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motion primitive.
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Only needs to be overwritten if the action space needs to be modified.
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"""
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try:
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return self.traj_gen_action_space
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except AttributeError:
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return self.get_traj_gen_action_space()
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def step(self, action: np.ndarray):
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""" This function generates a trajectory based on a MP and then does the usual loop over reset and step"""
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# agent to learn when to release the ball
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mp_params, env_spec_params = self._episode_callback(action)
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trajectory, velocity = self.get_trajectory(mp_params)
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trajectory_length = len(trajectory)
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rewards = np.zeros(shape=(trajectory_length,))
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if self.verbose >= 2:
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actions = np.zeros(shape=(trajectory_length,) + self.env.action_space.shape)
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observations = np.zeros(shape=(trajectory_length,) + self.env.observation_space.shape,
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dtype=self.env.observation_space.dtype)
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infos = dict()
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done = False
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for t, pos_vel in enumerate(zip(trajectory, velocity)):
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step_action = self.tracking_controller.get_action(pos_vel[0], pos_vel[1], self.current_pos,
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self.current_vel)
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step_action = self._step_callback(t, env_spec_params, step_action) # include possible callback info
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c_action = np.clip(step_action, self.env.action_space.low, self.env.action_space.high)
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# print('step/clipped action ratio: ', step_action/c_action)
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obs, c_reward, done, info = self.env.step(c_action)
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rewards[t] = c_reward
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if self.verbose >= 2:
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actions[t, :] = c_action
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observations[t, :] = obs
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for k, v in info.items():
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elems = infos.get(k, [None] * trajectory_length)
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elems[t] = v
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infos[k] = elems
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if self.render_kwargs:
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self.render(**self.render_kwargs)
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if done:
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break
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if self.replanning_schedule and self.replanning_schedule(self.current_pos, self.current_vel, obs, c_action,
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t + 1 + self.current_traj_steps):
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break
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infos.update({k: v[:t + 1] for k, v in infos.items()})
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self.current_traj_steps += t + 1
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if self.verbose >= 2:
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infos['trajectory'] = trajectory
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infos['step_actions'] = actions[:t + 1]
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infos['step_observations'] = observations[:t + 1]
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infos['step_rewards'] = rewards[:t + 1]
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infos['trajectory_length'] = t + 1
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trajectory_return = self.reward_aggregation(rewards[:t + 1])
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return obs, trajectory_return, done, infos
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def render(self, **kwargs):
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"""Only set render options here, such that they can be used during the rollout.
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This only needs to be called once"""
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self.render_kwargs = kwargs
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# self.env.render(mode=self.render_mode, **self.render_kwargs)
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self.env.render(**kwargs)
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def reset(self, **kwargs):
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self.current_traj_steps = 0
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super(BlackBoxWrapper, self).reset(**kwargs)
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def plot_trajs(self, des_trajs, des_vels):
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import matplotlib.pyplot as plt
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import matplotlib
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matplotlib.use('TkAgg')
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pos_fig = plt.figure('positions')
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vel_fig = plt.figure('velocities')
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for i in range(des_trajs.shape[1]):
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plt.figure(pos_fig.number)
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plt.subplot(des_trajs.shape[1], 1, i + 1)
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plt.plot(np.ones(des_trajs.shape[0]) * self.current_pos[i])
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plt.plot(des_trajs[:, i])
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plt.figure(vel_fig.number)
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plt.subplot(des_vels.shape[1], 1, i + 1)
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plt.plot(np.ones(des_trajs.shape[0]) * self.current_vel[i])
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plt.plot(des_vels[:, i])
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