94bfb32cc1
1. add policy.eval() in all test scripts' "watch performance" 2. remove dict return support for collector preprocess_fn 3. add `__contains__` and `pop` in batch: `key in batch`, `batch.pop(key, deft)` 4. exact n_episode for a list of n_episode limitation and save fake data in cache_buffer when self.buffer is None (#184) 5. fix tensorboard logging: h-axis stands for env step instead of gradient step; add test results into tensorboard 6. add test_returns (both GAE and nstep) 7. change the type-checking order in batch.py and converter.py in order to meet the most often case first 8. fix shape inconsistency for torch.Tensor in replay buffer 9. remove `**kwargs` in ReplayBuffer 10. remove default value in batch.split() and add merge_last argument (#185) 11. improve nstep efficiency 12. add max_batchsize in onpolicy algorithms 13. potential bugfix for subproc.wait 14. fix RecurrentActorProb 15. improve the code-coverage (from 90% to 95%) and remove the dead code 16. fix some incorrect type annotation The above improvement also increases the training FPS: on my computer, the previous version is only ~1800 FPS and after that, it can reach ~2050 (faster than v0.2.4.post1).
134 lines
4.3 KiB
Python
134 lines
4.3 KiB
Python
import cv2
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import gym
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import numpy as np
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from gym.spaces.box import Box
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from tianshou.data import Batch
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SIZE = 84
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FRAME = 4
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def create_atari_environment(name=None, sticky_actions=True,
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max_episode_steps=2000):
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game_version = 'v0' if sticky_actions else 'v4'
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name = '{}NoFrameskip-{}'.format(name, game_version)
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env = gym.make(name)
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env = env.env
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env = preprocessing(env, max_episode_steps=max_episode_steps)
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return env
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def preprocess_fn(obs=None, act=None, rew=None, done=None,
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obs_next=None, info=None, policy=None, **kwargs):
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if obs_next is not None:
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obs_next = np.reshape(obs_next, (-1, *obs_next.shape[2:]))
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obs_next = np.moveaxis(obs_next, 0, -1)
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obs_next = cv2.resize(obs_next, (SIZE, SIZE))
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obs_next = np.asanyarray(obs_next, dtype=np.uint8)
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obs_next = np.reshape(obs_next, (-1, FRAME, SIZE, SIZE))
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obs_next = np.moveaxis(obs_next, 1, -1)
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elif obs is not None:
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obs = np.reshape(obs, (-1, *obs.shape[2:]))
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obs = np.moveaxis(obs, 0, -1)
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obs = cv2.resize(obs, (SIZE, SIZE))
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obs = np.asanyarray(obs, dtype=np.uint8)
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obs = np.reshape(obs, (-1, FRAME, SIZE, SIZE))
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obs = np.moveaxis(obs, 1, -1)
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return Batch(obs=obs, act=act, rew=rew, done=done,
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obs_next=obs_next, info=info)
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class preprocessing(object):
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def __init__(self, env, frame_skip=4, terminal_on_life_loss=False,
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size=84, max_episode_steps=2000):
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self.max_episode_steps = max_episode_steps
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self.env = env
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self.terminal_on_life_loss = terminal_on_life_loss
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self.frame_skip = frame_skip
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self.size = size
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self.count = 0
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obs_dims = self.env.observation_space
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self.screen_buffer = [
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np.empty((obs_dims.shape[0], obs_dims.shape[1]), dtype=np.uint8),
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np.empty((obs_dims.shape[0], obs_dims.shape[1]), dtype=np.uint8)
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]
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self.game_over = False
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self.lives = 0
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@property
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def observation_space(self):
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return Box(low=0, high=255,
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shape=(self.size, self.size, self.frame_skip),
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dtype=np.uint8)
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def action_space(self):
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return self.env.action_space
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def reward_range(self):
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return self.env.reward_range
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def metadata(self):
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return self.env.metadata
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def close(self):
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return self.env.close()
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def reset(self):
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self.count = 0
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self.env.reset()
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self.lives = self.env.ale.lives()
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self._grayscale_obs(self.screen_buffer[0])
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self.screen_buffer[1].fill(0)
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return np.array([self._pool_and_resize()
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for _ in range(self.frame_skip)])
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def render(self, mode='human'):
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return self.env.render(mode)
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def step(self, action):
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total_reward = 0.
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observation = []
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for t in range(self.frame_skip):
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self.count += 1
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_, reward, terminal, info = self.env.step(action)
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total_reward += reward
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if self.terminal_on_life_loss:
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lives = self.env.ale.lives()
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is_terminal = terminal or lives < self.lives
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self.lives = lives
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else:
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is_terminal = terminal
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if is_terminal:
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break
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elif t >= self.frame_skip - 2:
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t_ = t - (self.frame_skip - 2)
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self._grayscale_obs(self.screen_buffer[t_])
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observation.append(self._pool_and_resize())
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if len(observation) == 0:
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observation = [self._pool_and_resize()
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for _ in range(self.frame_skip)]
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while len(observation) > 0 and \
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len(observation) < self.frame_skip:
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observation.append(observation[-1])
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terminal = self.count >= self.max_episode_steps
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return np.array(observation), total_reward, \
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(terminal or is_terminal), info
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def _grayscale_obs(self, output):
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self.env.ale.getScreenGrayscale(output)
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return output
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def _pool_and_resize(self):
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if self.frame_skip > 1:
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np.maximum(self.screen_buffer[0], self.screen_buffer[1],
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out=self.screen_buffer[0])
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return self.screen_buffer[0]
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