141 lines
4.4 KiB
Python
141 lines
4.4 KiB
Python
from abc import ABC, abstractmethod
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from collections import defaultdict
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import gym
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import numpy as np
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from alr_envs.utils.policies import get_policy_class
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class MPWrapper(gym.Wrapper, ABC):
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def __init__(self,
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env: gym.Env,
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num_dof: int,
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duration: int = 1,
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dt: float = None,
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post_traj_time: float = 0.,
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policy_type: str = None,
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weights_scale: float = 1.,
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render_mode: str = None,
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**mp_kwargs
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):
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super().__init__(env)
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self.num_dof = num_dof
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# self.num_basis = num_basis
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# self.duration = duration # seconds
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# dt = env.dt if hasattr(env, "dt") else dt
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assert dt is not None # this should never happen as MPWrapper is a base class
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self.post_traj_steps = int(post_traj_time / dt)
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self.mp = self.initialize_mp(num_dof, duration, dt, **mp_kwargs)
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self.weights_scale = weights_scale
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policy_class = get_policy_class(policy_type)
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self.policy = policy_class(env)
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# rendering
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self.render_mode = render_mode
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self.render_kwargs = {}
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# TODO: not yet final
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def __call__(self, params, contexts=None):
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params = np.atleast_2d(params)
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obs = []
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rewards = []
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dones = []
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infos = []
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# for p, c in zip(params, contexts):
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for p in params:
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# self.configure(c)
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# context = self.reset()
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ob, reward, done, info = self.step(p)
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obs.append(ob)
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rewards.append(reward)
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dones.append(done)
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infos.append(info)
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return obs, np.array(rewards), dones, infos
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def configure(self, context):
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self.env.configure(context)
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def reset(self):
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obs = self.env.reset()
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return obs
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def step(self, action: np.ndarray):
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""" This function generates a trajectory based on a DMP and then does the usual loop over reset and step"""
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trajectory, velocity = self.mp_rollout(action)
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if self.post_traj_steps > 0:
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trajectory = np.vstack([trajectory, np.tile(trajectory[-1, :], [self.post_traj_steps, 1])])
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velocity = np.vstack([velocity, np.zeros(shape=(self.post_traj_steps, self.mp.num_dimensions))])
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# self._trajectory = trajectory
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# self._velocity = velocity
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rewards = 0
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# infos = defaultdict(list)
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# TODO: @Max Why do we need this configure, states should be part of the model
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# TODO: Ask Onur if the context distribution needs to be outside the environment
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# TODO: For now create a new env with each context
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# TODO: Explicitly call reset before step to obtain context from obs?
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# self.env.configure(context)
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# obs = self.env.reset()
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info = {}
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for t, pos_vel in enumerate(zip(trajectory, velocity)):
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ac = self.policy.get_action(pos_vel[0], pos_vel[1])
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obs, rew, done, info = self.env.step(ac)
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rewards += rew
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# TODO return all dicts?
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# [infos[k].append(v) for k, v in info.items()]
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if self.render_mode:
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self.env.render(mode=self.render_mode, **self.render_kwargs)
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if done:
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break
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done = True
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return obs, rewards, done, info
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def render(self, mode='human', **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_mode = mode
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self.render_kwargs = kwargs
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# def __call__(self, actions):
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# return self.step(actions)
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# params = np.atleast_2d(params)
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# rewards = []
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# infos = []
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# for p, c in zip(params, contexts):
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# reward, info = self.rollout(p, c)
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# rewards.append(reward)
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# infos.append(info)
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#
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# return np.array(rewards), infos
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@abstractmethod
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def mp_rollout(self, action):
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"""
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Generate trajectory and velocity based on the MP
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Returns:
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trajectory/positions, velocity
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"""
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raise NotImplementedError()
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@abstractmethod
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def initialize_mp(self, num_dof: int, duration: int, dt: float, **kwargs):
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"""
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Create respective instance of MP
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Returns:
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MP instance
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"""
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raise NotImplementedError
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