138 lines
5.4 KiB
Python
138 lines
5.4 KiB
Python
import numpy as np
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from copy import deepcopy
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from tianshou.env import BaseVectorEnv
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from tianshou.data import Batch, ReplayBuffer
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from tianshou.utils import MovAvg
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class Collector(object):
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"""docstring for Collector"""
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def __init__(self, policy, env, buffer):
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super().__init__()
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self.env = env
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self.env_num = 1
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self.buffer = buffer
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self.policy = policy
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self.process_fn = policy.process_fn
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self.multi_env = isinstance(env, BaseVectorEnv)
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if self.multi_env:
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self.env_num = len(env)
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if isinstance(self.buffer, list):
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assert len(self.buffer) == self.env_num,\
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'# of data buffer does not match the # of input env.'
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elif isinstance(self.buffer, ReplayBuffer):
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self.buffer = [deepcopy(buffer) for _ in range(self.env_num)]
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else:
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raise TypeError('The buffer in data collector is invalid!')
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self.reset_env()
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self.clear_buffer()
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# state over batch is either a list, an np.ndarray, or torch.Tensor
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self.state = None
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self.stat_reward = MovAvg()
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self.stat_length = MovAvg()
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def clear_buffer(self):
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if self.multi_env:
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for b in self.buffer:
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b.reset()
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else:
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self.buffer.reset()
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def reset_env(self):
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self._obs = self.env.reset()
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self._act = self._rew = self._done = self._info = None
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if self.multi_env:
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self.reward = np.zeros(self.env_num)
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self.length = np.zeros(self.env_num)
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else:
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self.reward, self.length = 0, 0
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def collect(self, n_step=0, n_episode=0):
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assert sum([(n_step > 0), (n_episode > 0)]) == 1,\
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"One and only one collection number specification permitted!"
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cur_step = 0
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cur_episode = np.zeros(self.env_num) if self.multi_env else 0
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while True:
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if self.multi_env:
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batch_data = Batch(
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obs=self._obs, act=self._act, rew=self._rew,
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done=self._done, obs_next=None, info=self._info)
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else:
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batch_data = Batch(
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obs=[self._obs], act=[self._act], rew=[self._rew],
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done=[self._done], obs_next=None, info=[self._info])
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result = self.policy.act(batch_data, self.state)
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self.state = result.state
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self._act = result.act
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obs_next, self._rew, self._done, self._info = self.env.step(
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self._act)
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cur_step += 1
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self.length += 1
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self.reward += self._rew
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if self.multi_env:
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for i in range(self.env_num):
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if n_episode > 0 and \
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cur_episode[i] < n_episode or n_episode == 0:
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self.buffer[i].add(
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self._obs[i], self._act[i], self._rew[i],
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self._done[i], obs_next[i], self._info[i])
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if self._done[i]:
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cur_episode[i] += 1
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self.stat_reward.add(self.reward[i])
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self.stat_length.add(self.length[i])
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self.reward[i], self.length[i] = 0, 0
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if isinstance(self.state, list):
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self.state[i] = None
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else:
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self.state[i] = self.state[i] * 0
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if hasattr(self.state, 'detach'):
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# remove ref in torch
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self.state = self.state.detach()
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if n_episode > 0 and (cur_episode >= n_episode).all():
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break
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else:
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self.buffer.add(
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self._obs, self._act[0], self._rew,
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self._done, obs_next, self._info)
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if self._done:
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cur_episode += 1
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self.stat_reward.add(self.reward)
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self.stat_length.add(self.length)
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self.reward, self.length = 0, 0
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self.state = None
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if n_episode > 0 and cur_episode >= n_episode:
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break
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if n_step > 0 and cur_step >= n_step:
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break
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self._obs = obs_next
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self._obs = obs_next
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def sample(self, batch_size):
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if self.multi_env:
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if batch_size > 0:
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lens = [len(b) for b in self.buffer]
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total = sum(lens)
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ib = np.random.choice(
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total, batch_size, p=np.array(lens) / total)
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else:
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ib = np.array([])
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batch_data = Batch()
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for i, b in enumerate(self.buffer):
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cur_batch = (ib == i).sum()
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if batch_size and cur_batch or batch_size <= 0:
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batch, indice = b.sample(cur_batch)
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batch = self.process_fn(batch, b, indice)
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batch_data.append(batch)
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else:
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batch_data, indice = self.buffer.sample(batch_size)
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batch_data = self.process_fn(batch_data, self.buffer, indice)
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return batch_data
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def stat(self):
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return {
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'reward': self.stat_reward.get(),
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'length': self.stat_length.get(),
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}
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