Source code for rlzoo.common.env_wrappers

"""Env wrappers
Most common wrappers can be checked from following links for usage: 

`https://pypi.org/project/gym-vec-env`

`https://github.com/openai/baselines/blob/master/baselines/common/wrappers.py`
"""
from collections import deque
from functools import partial
from multiprocessing import Pipe, Process, cpu_count
from sys import platform

import cv2
import gym
import numpy as np
from gym import spaces
from gym.wrappers import FlattenDictWrapper

from rlzoo.common.env_list import get_envlist

__all__ = (
    'build_env',  # build env
    'TimeLimit',  # Time limit wrapper
    'NoopResetEnv',  # Run random number of no-ops on reset
    'FireResetEnv',  # Reset wrapper for envs with fire action
    'EpisodicLifeEnv',  # end-of-life == end-of-episode wrapper
    'MaxAndSkipEnv',  # skip frame wrapper
    'ClipRewardEnv',  # clip reward wrapper
    'WarpFrame',  # warp observation wrapper
    'FrameStack',  # stack frame wrapper
    'LazyFrames',  # lazy store wrapper
    'RewardShaping',  # reward shaping
    'SubprocVecEnv',  # vectorized env wrapper
    'VecFrameStack',  # stack frames in vectorized env
    'Monitor',  # Episode reward and length monitor
    'NormalizedActions',  # normalized action to actual space
    'DmObsTrans',  # translate observations in dm_control environments
)
cv2.ocl.setUseOpenCL(False)


[docs]def build_env(env_id, env_type, vectorized=False, seed=0, reward_shaping=None, nenv=1, **kwargs): """ Build env based on options :param env_id: (str) environment id :param env_type: (str) atari, classic_control, box2d :param vectorized: (bool) whether sampling parrallel :param seed: (int) random seed for env :param reward_shaping: (callable) callable function for reward shaping :param nenv: (int) how many processes will be used in sampling :param kwargs: (dict) :param max_episode_steps: (int) the maximum episode steps """ nenv = nenv or cpu_count() // (1 + (platform == 'darwin')) stack = env_type == 'atari' if nenv > 1: if vectorized: env = _make_vec_env(env_id, env_type, nenv, seed, reward_shaping, stack, **kwargs) else: env = [] for _ in range(nenv): single_env = _make_env(env_id, env_type, seed, reward_shaping, stack, **kwargs) env.append(single_env) # get env as a list of same single env else: env = _make_env(env_id, env_type, seed, reward_shaping, stack, **kwargs) return env
def check_name_in_list(env_id, env_type): """ Check if env_id exists in the env_type list """ env_list = get_envlist(env_type) if env_id not in env_list: print('Env ID {:s} Not Found In {:s}!'.format(env_id, env_type)) else: print('Env ID {:s} Exists!'.format(env_id)) def _make_env(env_id, env_type, seed, reward_shaping, frame_stack, **kwargs): """Make single env""" check_name_in_list(env_id, env_type) # check existence of env_id in env_type if env_type == 'atari': env = gym.make(env_id) env = NoopResetEnv(env, noop_max=30) if 'NoFrameskip' in env.spec.id: env = MaxAndSkipEnv(env, skip=4) env = Monitor(env) # deepmind wrap env = EpisodicLifeEnv(env) if 'FIRE' in env.unwrapped.get_action_meanings(): env = FireResetEnv(env) env = WarpFrame(env) env = ClipRewardEnv(env) if frame_stack: env = FrameStack(env, 4) elif env_type in ['classic_control', 'box2d', 'mujoco']: env = gym.make(env_id).unwrapped max_episode_steps = kwargs.get('max_episode_steps') if max_episode_steps is not None: env = TimeLimit(env.unwrapped, max_episode_steps) env = Monitor(env) elif env_type == 'robotics': env = gym.make(env_id) env = FlattenDictWrapper(env, ['observation', 'desired_goal']) env = Monitor(env, info_keywords=('is_success',)) elif env_type == 'dm_control': env = gym.make('dm2gym:' + env_id, environment_kwargs={'flat_observation': True}) env = DmObsTrans(env) elif env_type == 'rlbench': from rlzoo.common.build_rlbench_env import RLBenchEnv state_type = kwargs.get('state_type') env = RLBenchEnv(env_id) if state_type is None else RLBenchEnv(env_id, state_type) else: raise NotImplementedError if reward_shaping is not None: if callable(reward_shaping): env = RewardShaping(env, reward_shaping) else: raise ValueError('reward_shaping parameter must be callable') env.seed(seed) return env def _make_vec_env(env_id, env_type, nenv, seed, reward_shaping, frame_stack, **kwargs): """Make vectorized env""" env = SubprocVecEnv([partial( _make_env, env_id, env_type, seed + i, reward_shaping, False, **kwargs ) for i in range(nenv)]) if frame_stack: env = VecFrameStack(env, 4) return env
[docs]class DmObsTrans(gym.Wrapper): """ Observation process for DeepMind Control Suite environments """ def __init__(self, env): self.env = env super(DmObsTrans, self).__init__(env) self.__need_trans = False if isinstance(self.observation_space, gym.spaces.dict.Dict): self.observation_space = self.observation_space['observations'] self.__need_trans = True
[docs] def step(self, ac): observation, reward, done, info = self.env.step(ac) if self.__need_trans: observation = observation['observations'] return observation, reward, done, info
[docs] def reset(self, **kwargs): observation = self.env.reset(**kwargs) if self.__need_trans: observation = observation['observations'] return observation
[docs]class TimeLimit(gym.Wrapper): def __init__(self, env, max_episode_steps=None): self.env = env super(TimeLimit, self).__init__(env) self._max_episode_steps = max_episode_steps self._elapsed_steps = 0
[docs] def step(self, ac): observation, reward, done, info = self.env.step(ac) self._elapsed_steps += 1 if self._elapsed_steps >= self._max_episode_steps: done = True info['TimeLimit.truncated'] = True return observation, reward, done, info
[docs] def reset(self, **kwargs): self._elapsed_steps = 0 return self.env.reset(**kwargs)
[docs]class NoopResetEnv(gym.Wrapper): def __init__(self, env, noop_max=30): """Sample initial states by taking random number of no-ops on reset. No-op is assumed to be action 0. """ super(NoopResetEnv, self).__init__(env) self.noop_max = noop_max self.override_num_noops = None self.noop_action = 0 assert env.unwrapped.get_action_meanings()[0] == 'NOOP'
[docs] def reset(self, **kwargs): """ Do no-op action for a number of steps in [1, noop_max].""" self.env.reset(**kwargs) if self.override_num_noops is not None: noops = self.override_num_noops else: noops = self.unwrapped.np_random.randint(1, self.noop_max + 1) assert noops > 0 obs = None for _ in range(noops): obs, _, done, _ = self.env.step(self.noop_action) if done: obs = self.env.reset(**kwargs) return obs
[docs] def step(self, ac): return self.env.step(ac)
[docs]class FireResetEnv(gym.Wrapper): def __init__(self, env): """Take action on reset for environments that are fixed until firing.""" super(FireResetEnv, self).__init__(env) assert env.unwrapped.get_action_meanings()[1] == 'FIRE' assert len(env.unwrapped.get_action_meanings()) >= 3
[docs] def reset(self, **kwargs): self.env.reset(**kwargs) obs, _, done, _ = self.env.step(1) if done: self.env.reset(**kwargs) obs, _, done, _ = self.env.step(2) if done: self.env.reset(**kwargs) return obs
[docs] def step(self, ac): return self.env.step(ac)
[docs]class EpisodicLifeEnv(gym.Wrapper): def __init__(self, env): """Make end-of-life == end-of-episode, but only reset on true game over. Done by DeepMind for the DQN and co. since it helps value estimation. """ super(EpisodicLifeEnv, self).__init__(env) self.lives = 0 self.was_real_done = True
[docs] def step(self, action): obs, reward, done, info = self.env.step(action) self.was_real_done = done # check current lives, make loss of life terminal, # then update lives to handle bonus lives lives = self.env.unwrapped.ale.lives() if 0 < lives < self.lives: # for Qbert sometimes we stay in lives == 0 condition for a few # frames so it's important to keep lives > 0, so that we only reset # once the environment advertises done. done = True self.lives = lives return obs, reward, done, info
[docs] def reset(self, **kwargs): """Reset only when lives are exhausted. This way all states are still reachable even though lives are episodic, and the learner need not know about any of this behind-the-scenes. """ if self.was_real_done: obs = self.env.reset(**kwargs) else: # no-op step to advance from terminal/lost life state obs, _, _, _ = self.env.step(0) self.lives = self.env.unwrapped.ale.lives() return obs
[docs]class MaxAndSkipEnv(gym.Wrapper): def __init__(self, env, skip=4): """Return only every `skip`-th frame""" super(MaxAndSkipEnv, self).__init__(env) # most recent raw observations (for max pooling across time steps) shape = (2,) + env.observation_space.shape self._obs_buffer = np.zeros(shape, dtype=np.uint8) self._skip = skip
[docs] def step(self, action): """Repeat action, sum reward, and max over last observations.""" total_reward = 0.0 done = info = None for i in range(self._skip): obs, reward, done, info = self.env.step(action) if i == self._skip - 2: self._obs_buffer[0] = obs if i == self._skip - 1: self._obs_buffer[1] = obs total_reward += reward if done: break # Note that the observation on the done=True frame doesn't matter max_frame = self._obs_buffer.max(axis=0) return max_frame, total_reward, done, info
[docs] def reset(self, **kwargs): return self.env.reset(**kwargs)
[docs]class ClipRewardEnv(gym.RewardWrapper): def __init__(self, env): super(ClipRewardEnv, self).__init__(env)
[docs] def reward(self, reward): """Bin reward to {+1, 0, -1} by its sign.""" return np.sign(reward)
[docs]class WarpFrame(gym.ObservationWrapper): def __init__(self, env, width=84, height=84, grayscale=True): """Warp frames to 84x84 as done in the Nature paper and later work.""" super(WarpFrame, self).__init__(env) self.width = width self.height = height self.grayscale = grayscale shape = (self.height, self.width, 1 if self.grayscale else 3) self.observation_space = spaces.Box(low=0, high=255, shape=shape, dtype=np.uint8)
[docs] def observation(self, frame): if self.grayscale: frame = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY) size = (self.width, self.height) frame = cv2.resize(frame, size, interpolation=cv2.INTER_AREA) if self.grayscale: frame = np.expand_dims(frame, -1) return frame
[docs]class FrameStack(gym.Wrapper): def __init__(self, env, k): """Stack k last frames. Returns lazy array, which is much more memory efficient. See Also `LazyFrames` """ super(FrameStack, self).__init__(env) self.k = k self.frames = deque([], maxlen=k) shp = env.observation_space.shape shape = shp[:-1] + (shp[-1] * k,) self.observation_space = spaces.Box(low=0, high=255, shape=shape, dtype=env.observation_space.dtype)
[docs] def reset(self): ob = self.env.reset() for _ in range(self.k): self.frames.append(ob) return np.asarray(self._get_ob())
[docs] def step(self, action): ob, reward, done, info = self.env.step(action) self.frames.append(ob) return np.asarray(self._get_ob()), reward, done, info
def _get_ob(self): assert len(self.frames) == self.k return LazyFrames(list(self.frames))
[docs]class LazyFrames(object): def __init__(self, frames): """This object ensures that common frames between the observations are only stored once. It exists purely to optimize memory usage which can be huge for DQN's 1M frames replay buffers. This object should only be converted to numpy array before being passed to the model. You'd not believe how complex the previous solution was. """ self._frames = frames self._out = None def _force(self): if self._out is None: self._out = np.concatenate(self._frames, axis=-1) self._frames = None return self._out def __array__(self, dtype=None): out = self._force() if dtype is not None: out = out.astype(dtype) return out def __len__(self): return len(self._force()) def __getitem__(self, i): return self._force()[i]
[docs]class RewardShaping(gym.RewardWrapper): """Shaping the reward For reward scale, func can be `lambda r: r * scale` """ def __init__(self, env, func): super(RewardShaping, self).__init__(env) self.func = func
[docs] def reward(self, reward): return self.func(reward)
[docs]class VecFrameStack(object): def __init__(self, env, k): self.env = env self.k = k self.action_space = env.action_space self.frames = deque([], maxlen=k) shp = env.observation_space.shape shape = shp[:-1] + (shp[-1] * k,) self.observation_space = spaces.Box(low=0, high=255, shape=shape, dtype=env.observation_space.dtype)
[docs] def reset(self): ob = self.env.reset() for _ in range(self.k): self.frames.append(ob) return np.asarray(self._get_ob())
[docs] def step(self, action): ob, reward, done, info = self.env.step(action) self.frames.append(ob) return np.asarray(self._get_ob()), reward, done, info
def _get_ob(self): assert len(self.frames) == self.k return LazyFrames(list(self.frames))
def _worker(remote, parent_remote, env_fn_wrapper): parent_remote.close() env = env_fn_wrapper.x() while True: cmd, data = remote.recv() if cmd == 'step': ob, reward, done, info = env.step(data) if done: ob = env.reset() remote.send((ob, reward, done, info)) elif cmd == 'reset': ob = env.reset() remote.send(ob) elif cmd == 'reset_task': ob = env._reset_task() remote.send(ob) elif cmd == 'close': remote.close() break elif cmd == 'get_spaces': remote.send((env.observation_space, env.action_space)) else: raise NotImplementedError class CloudpickleWrapper(object): """ Uses cloudpickle to serialize contents """ def __init__(self, x): self.x = x def __getstate__(self): import cloudpickle return cloudpickle.dumps(self.x) def __setstate__(self, ob): import pickle self.x = pickle.loads(ob)
[docs]class SubprocVecEnv(object): def __init__(self, env_fns): """ envs: list of gym environments to run in subprocesses """ self.num_envs = len(env_fns) self.waiting = False self.closed = False nenvs = len(env_fns) self.nenvs = nenvs self.remotes, self.work_remotes = zip(*[Pipe() for _ in range(nenvs)]) zipped_args = zip(self.work_remotes, self.remotes, env_fns) self.ps = [ Process(target=_worker, args=(work_remote, remote, CloudpickleWrapper(env_fn))) for (work_remote, remote, env_fn) in zipped_args ] for p in self.ps: # if the main process crashes, we should not cause things to hang p.daemon = True p.start() for remote in self.work_remotes: remote.close() self.remotes[0].send(('get_spaces', None)) observation_space, action_space = self.remotes[0].recv() self.observation_space = observation_space self.action_space = action_space def _step_async(self, actions): """ Tell all the environments to start taking a step with the given actions. Call step_wait() to get the results of the step. You should not call this if a step_async run is already pending. """ for remote, action in zip(self.remotes, actions): remote.send(('step', action)) self.waiting = True def _step_wait(self): """ Wait for the step taken with step_async(). Returns (obs, rews, dones, infos): - obs: an array of observations, or a tuple of arrays of observations. - rews: an array of rewards - dones: an array of "episode done" booleans - infos: a sequence of info objects """ results = [remote.recv() for remote in self.remotes] self.waiting = False obs, rews, dones, infos = zip(*results) return np.stack(obs), np.stack(rews), np.stack(dones), infos
[docs] def reset(self): """ Reset all the environments and return an array of observations, or a tuple of observation arrays. If step_async is still doing work, that work will be cancelled and step_wait() should not be called until step_async() is invoked again. """ for remote in self.remotes: remote.send(('reset', None)) return np.stack([remote.recv() for remote in self.remotes])
def _reset_task(self): for remote in self.remotes: remote.send(('reset_task', None)) return np.stack([remote.recv() for remote in self.remotes])
[docs] def close(self): if self.closed: return if self.waiting: for remote in self.remotes: remote.recv() for remote in self.remotes: remote.send(('close', None)) for p in self.ps: p.join() self.closed = True
def __len__(self): return self.nenvs
[docs] def step(self, actions): self._step_async(actions) return self._step_wait()
[docs]class Monitor(gym.Wrapper): def __init__(self, env, info_keywords=None): super(Monitor, self).__init__(env) self._monitor_rewards = None self._info_keywords = info_keywords or []
[docs] def reset(self, **kwargs): self._monitor_rewards = [] return self.env.reset(**kwargs)
[docs] def step(self, action): o_, r, done, info = self.env.step(action) self._monitor_rewards.append(r) if done: info['episode'] = { 'r': sum(self._monitor_rewards), 'l': len(self._monitor_rewards) } for keyword in self._info_keywords: info['episode'][keyword] = info[keyword] return o_, r, done, info
[docs]class NormalizedActions(gym.ActionWrapper): def _action(self, action): low = self.action_space.low high = self.action_space.high action = low + (action + 1.0) * 0.5 * (high - low) action = np.clip(action, low, high) return action def _reverse_action(self, action): low = self.action_space.low high = self.action_space.high action = 2 * (action - low) / (high - low) - 1 action = np.clip(action, low, high) return action
def close_env(env): """ close environment or environment list """ try: env.close() except: pass try: for e in env: e.close() except: pass