mirror of
https://github.com/wassname/ray.git
synced 2026-07-09 13:08:40 +08:00
Yuhong Guo
216 lines
7.0 KiB
Python
216 lines
7.0 KiB
Python
import numpy as np
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from collections import deque
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import gym
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from gym import spaces
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import cv2
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cv2.ocl.setUseOpenCL(False)
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def is_atari(env):
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return hasattr(env, "unwrapped") and hasattr(env.unwrapped, "ale")
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class NoopResetEnv(gym.Wrapper):
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def __init__(self, env, noop_max=30, random_starts=False):
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"""Sample initial states by taking random number of no-ops on reset.
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No-op is assumed to be action 0.
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"""
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gym.Wrapper.__init__(self, env)
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self.noop_max = noop_max
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self.override_num_noops = None
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self.noop_action = 0
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self.random_starts = random_starts
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assert env.unwrapped.get_action_meanings()[0] == 'NOOP'
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def reset(self, **kwargs):
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""" Do no-op action for a number of steps in [1, noop_max]."""
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self.env.reset(**kwargs)
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if self.override_num_noops is not None:
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noops = self.override_num_noops
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else:
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noops = self.unwrapped.np_random.randint(1, self.noop_max + 1)
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assert noops > 0
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obs = None
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for _ in range(noops):
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if self.random_starts:
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action = np.random.randint(self.env.action_space.n)
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else:
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action = self.noop_action
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obs, _, done, _ = self.env.step(action)
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if done:
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obs = self.env.reset(**kwargs)
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return obs
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def step(self, ac):
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return self.env.step(ac)
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class ClipRewardEnv(gym.RewardWrapper):
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def __init__(self, env):
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gym.RewardWrapper.__init__(self, env)
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def reward(self, reward):
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"""Bin reward to {+1, 0, -1} by its sign."""
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return np.sign(reward)
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class FireResetEnv(gym.Wrapper):
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def __init__(self, env):
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"""Take action on reset.
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For environments that are fixed until firing."""
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gym.Wrapper.__init__(self, env)
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assert env.unwrapped.get_action_meanings()[1] == 'FIRE'
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assert len(env.unwrapped.get_action_meanings()) >= 3
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def reset(self, **kwargs):
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self.env.reset(**kwargs)
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obs, _, done, _ = self.env.step(1)
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if done:
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self.env.reset(**kwargs)
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obs, _, done, _ = self.env.step(2)
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if done:
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self.env.reset(**kwargs)
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return obs
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def step(self, ac):
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return self.env.step(ac)
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class EpisodicLifeEnv(gym.Wrapper):
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def __init__(self, env):
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"""Make end-of-life == end-of-episode, but only reset on true game over.
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Done by DeepMind for the DQN and co. since it helps value estimation.
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"""
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gym.Wrapper.__init__(self, env)
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self.lives = 0
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self.was_real_done = True
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def step(self, action):
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obs, reward, done, info = self.env.step(action)
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self.was_real_done = done
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# check current lives, make loss of life terminal,
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# then update lives to handle bonus lives
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lives = self.env.unwrapped.ale.lives()
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if lives < self.lives and lives > 0:
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# for Qbert sometimes we stay in lives == 0 condtion for a few
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# frames so its important to keep lives > 0, so that we only reset
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# once the environment advertises done.
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done = True
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self.lives = lives
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return obs, reward, done, info
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def reset(self, **kwargs):
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"""Reset only when lives are exhausted.
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This way all states are still reachable even though lives are episodic,
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and the learner need not know about any of this behind-the-scenes.
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"""
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if self.was_real_done:
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obs = self.env.reset(**kwargs)
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else:
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# no-op step to advance from terminal/lost life state
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obs, _, _, _ = self.env.step(0)
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self.lives = self.env.unwrapped.ale.lives()
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return obs
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class MaxAndSkipEnv(gym.Wrapper):
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def __init__(self, env, skip=4):
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"""Return only every `skip`-th frame"""
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gym.Wrapper.__init__(self, env)
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# most recent raw observations (for max pooling across time steps)
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self._obs_buffer = np.zeros(
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(2, ) + env.observation_space.shape, dtype=np.uint8)
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self._skip = skip
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def step(self, action):
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"""Repeat action, sum reward, and max over last observations."""
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total_reward = 0.0
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done = None
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for i in range(self._skip):
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obs, reward, done, info = self.env.step(action)
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if i == self._skip - 2:
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self._obs_buffer[0] = obs
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if i == self._skip - 1:
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self._obs_buffer[1] = obs
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total_reward += reward
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if done:
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break
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# Note that the observation on the done=True frame
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# doesn't matter
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max_frame = self._obs_buffer.max(axis=0)
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return max_frame, total_reward, done, info
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def reset(self, **kwargs):
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return self.env.reset(**kwargs)
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class WarpFrame(gym.ObservationWrapper):
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def __init__(self, env, dim):
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"""Warp frames to the specified size (dim x dim)."""
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gym.ObservationWrapper.__init__(self, env)
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self.width = dim # in rllib we use 80
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self.height = dim
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self.observation_space = spaces.Box(
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low=0,
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high=255,
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shape=(self.height, self.width, 1),
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dtype=np.float32)
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def observation(self, frame):
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frame = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
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frame = cv2.resize(
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frame, (self.width, self.height), interpolation=cv2.INTER_AREA)
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return frame[:, :, None]
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class FrameStack(gym.Wrapper):
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def __init__(self, env, k):
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"""Stack k last frames."""
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gym.Wrapper.__init__(self, env)
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self.k = k
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self.frames = deque([], maxlen=k)
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shp = env.observation_space.shape
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self.observation_space = spaces.Box(
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low=0,
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high=255,
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shape=(shp[0], shp[1], shp[2] * k),
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dtype=np.float32)
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def reset(self):
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ob = self.env.reset()
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for _ in range(self.k):
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self.frames.append(ob)
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return self._get_ob()
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def step(self, action):
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ob, reward, done, info = self.env.step(action)
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self.frames.append(ob)
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return self._get_ob(), reward, done, info
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def _get_ob(self):
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assert len(self.frames) == self.k
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return np.concatenate(self.frames, axis=2)
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def wrap_deepmind(env, random_starts=True, dim=80):
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"""Configure environment for DeepMind-style Atari.
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Note that we assume reward clipping is done outside the wrapper.
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Args:
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random_starts (bool): Start with random actions instead of noops.
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dim (int): Dimension to resize observations to (dim x dim).
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"""
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env = NoopResetEnv(env, noop_max=30, random_starts=random_starts)
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if 'NoFrameskip' in env.spec.id:
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env = MaxAndSkipEnv(env, skip=4)
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env = EpisodicLifeEnv(env)
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if 'FIRE' in env.unwrapped.get_action_meanings():
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env = FireResetEnv(env)
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env = WarpFrame(env, dim)
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# env = ClipRewardEnv(env) # reward clipping is handled by DQN replay
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env = FrameStack(env, 4)
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return env
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