class Collector(object):
"""The :class:`~tianshou.data.Collector` enables the policy to interact
with different types of environments conveniently.
:param policy: an instance of the :class:`~tianshou.policy.BasePolicy`
class.
:param env: a ``gym.Env`` environment or an instance of the
:class:`~tianshou.env.BaseVectorEnv` class.
:param buffer: an instance of the :class:`~tianshou.data.ReplayBuffer`
class, or a list of :class:`~tianshou.data.ReplayBuffer`. If set to
``None``, it will automatically assign a small-size
:class:`~tianshou.data.ReplayBuffer`.
:param function preprocess_fn: a function called before the data has been
added to the buffer, see issue #42, defaults to ``None``.
:param int stat_size: for the moving average of recording speed, defaults
to 100.
The ``preprocess_fn`` is a function called before the data has been added
to the buffer with batch format, which receives up to 7 keys as listed in
:class:`~tianshou.data.Batch`. It will receive with only ``obs`` when the
collector resets the environment. It returns either a dict or a
:class:`~tianshou.data.Batch` with the modified keys and values. Examples
are in "test/base/test_collector.py".
Example:
::
policy = PGPolicy(...) # or other policies if you wish
env = gym.make('CartPole-v0')
replay_buffer = ReplayBuffer(size=10000)
# here we set up a collector with a single environment
collector = Collector(policy, env, buffer=replay_buffer)
# the collector supports vectorized environments as well
envs = VectorEnv([lambda: gym.make('CartPole-v0') for _ in range(3)])
buffers = [ReplayBuffer(size=5000) for _ in range(3)]
# you can also pass a list of replay buffer to collector, for multi-env
# collector = Collector(policy, envs, buffer=buffers)
collector = Collector(policy, envs, buffer=replay_buffer)
# collect at least 3 episodes
collector.collect(n_episode=3)
# collect 1 episode for the first env, 3 for the third env
collector.collect(n_episode=[1, 0, 3])
# collect at least 2 steps
collector.collect(n_step=2)
# collect episodes with visual rendering (the render argument is the
# sleep time between rendering consecutive frames)
collector.collect(n_episode=1, render=0.03)
# sample data with a given number of batch-size:
batch_data = collector.sample(batch_size=64)
# policy.learn(batch_data) # btw, vanilla policy gradient only
# supports on-policy training, so here we pick all data in the buffer
batch_data = collector.sample(batch_size=0)
policy.learn(batch_data)
# on-policy algorithms use the collected data only once, so here we
# clear the buffer
collector.reset_buffer()
For the scenario of collecting data from multiple environments to a single
buffer, the cache buffers will turn on automatically. It may return the
data more than the given limitation.
.. note::
Please make sure the given environment has a time limitation.
"""
def __init__(self,
policy: BasePolicy,
env: Union[gym.Env, BaseVectorEnv],
buffer: Optional[Union[ReplayBuffer, List[ReplayBuffer]]]
= None,
preprocess_fn: Callable[[Any], Union[dict, Batch]] = None,
stat_size: Optional[int] = 100,
**kwargs) -> None:
super().__init__()
self.env = env
self.env_num = 1
self.collect_time = 0
self.collect_step = 0
self.collect_episode = 0
self.buffer = buffer
self.policy = policy
self.preprocess_fn = preprocess_fn
# if preprocess_fn is None:
# def _prep(**kwargs):
# return kwargs
# self.preprocess_fn = _prep
self.process_fn = policy.process_fn
self._multi_env = isinstance(env, BaseVectorEnv)
self._multi_buf = False # True if buf is a list
# need multiple cache buffers only if storing in one buffer
self._cached_buf = []
if self._multi_env:
self.env_num = len(env)
if isinstance(self.buffer, list):
assert len(self.buffer) == self.env_num, \
'The number of data buffer does not match the number of ' \
'input env.'
self._multi_buf = True
elif isinstance(self.buffer, ReplayBuffer) or self.buffer is None:
self._cached_buf = [
ListReplayBuffer() for _ in range(self.env_num)]
else:
raise TypeError('The buffer in data collector is invalid!')
self.stat_size = stat_size
self.reset()
def reset(self) -> None:
"""Reset all related variables in the collector."""
self.reset_env()
self.reset_buffer()
# state over batch is either a list, an np.ndarray, or a torch.Tensor
self.state = None
self.step_speed = MovAvg(self.stat_size)
self.episode_speed = MovAvg(self.stat_size)
self.collect_step = 0
self.collect_episode = 0
self.collect_time = 0
def reset_buffer(self) -> None:
"""Reset the main data buffer."""
if self._multi_buf:
for b in self.buffer:
b.reset()
else:
if self.buffer is not None:
self.buffer.reset()
def get_env_num(self) -> int:
"""Return the number of environments the collector have."""
return self.env_num
def reset_env(self) -> None:
"""Reset all of the environment(s)' states and reset all of the cache
buffers (if need).
"""
self._obs = self.env.reset()
if not self._multi_env:
self._obs = self._make_batch(self._obs)
if self.preprocess_fn:
self._obs = self.preprocess_fn(obs=self._obs).get('obs', self._obs)
self._act = self._rew = self._done = self._info = None
if self._multi_env:
self.reward = np.zeros(self.env_num)
self.length = np.zeros(self.env_num)
else:
self.reward, self.length = 0, 0
for b in self._cached_buf:
b.reset()
def seed(self, seed: Optional[Union[int, List[int]]] = None) -> None:
"""Reset all the seed(s) of the given environment(s)."""
if hasattr(self.env, 'seed'):
return self.env.seed(seed)
def render(self, **kwargs) -> None:
"""Render all the environment(s)."""
if hasattr(self.env, 'render'):
return self.env.render(**kwargs)
def close(self) -> None:
"""Close the environment(s)."""
if hasattr(self.env, 'close'):
self.env.close()
def _make_batch(self, data: Any) -> np.ndarray:
"""Return [data]."""
if isinstance(data, np.ndarray):
return data[None]
else:
return np.array([data])
def _reset_state(self, id: Union[int, List[int]]) -> None:
"""Reset self.state[id]."""
if self.state is None:
return
if isinstance(self.state, list):
self.state[id] = None
elif isinstance(self.state, (dict, Batch)):
for k in self.state.keys():
if isinstance(self.state[k], list):
self.state[k][id] = None
elif isinstance(self.state[k], (torch.Tensor, np.ndarray)):
self.state[k][id] = 0
elif isinstance(self.state, (torch.Tensor, np.ndarray)):
self.state[id] = 0
def collect(self,
n_step: int = 0,
n_episode: Union[int, List[int]] = 0,
random: bool = False,
render: Optional[float] = None,
log_fn: Optional[Callable[[dict], None]] = None
) -> Dict[str, float]:
"""Collect a specified number of step or episode.
:param int n_step: how many steps you want to collect.
:param n_episode: how many episodes you want to collect (in each
environment).
:type n_episode: int or list
:param bool random: whether to use random policy for collecting data,
defaults to ``False``.
:param float render: the sleep time between rendering consecutive
frames, defaults to ``None`` (no rendering).
:param function log_fn: a function which receives env info, typically
for tensorboard logging.
.. note::
One and only one collection number specification is permitted,
either ``n_step`` or ``n_episode``.
:return: A dict including the following keys
* ``n/ep`` the collected number of episodes.
* ``n/st`` the collected number of steps.
* ``v/st`` the speed of steps per second.
* ``v/ep`` the speed of episode per second.
* ``rew`` the mean reward over collected episodes.
* ``len`` the mean length over collected episodes.
"""
warning_count = 0
if not self._multi_env:
n_episode = np.sum(n_episode)
start_time = time.time()
assert sum([(n_step != 0), (n_episode != 0)]) == 1, \
"One and only one collection number specification is permitted!"
cur_step = 0
cur_episode = np.zeros(self.env_num) if self._multi_env else 0
reward_sum = 0
length_sum = 0
while True:
if warning_count >= 100000:
warnings.warn(
'There are already many steps in an episode. '
'You should add a time limitation to your environment!',
Warning)
batch = Batch(
obs=self._obs, act=self._act, rew=self._rew,
done=self._done, obs_next=None, info=self._info,
policy=None)
if random:
action_space = self.env.action_space
if isinstance(action_space, list):
result = Batch(act=[a.sample() for a in action_space])
else:
result = Batch(act=self._make_batch(action_space.sample()))
else:
with torch.no_grad():
result = self.policy(batch, self.state)
self.state = result.get('state', None)
self._policy = to_numpy(result.policy) \
if hasattr(result, 'policy') else [{}] * self.env_num
self._act = to_numpy(result.act)
obs_next, self._rew, self._done, self._info = self.env.step(
self._act if self._multi_env else self._act[0])
if not self._multi_env:
obs_next = self._make_batch(obs_next)
self._rew = self._make_batch(self._rew)
self._done = self._make_batch(self._done)
self._info = self._make_batch(self._info)
if log_fn:
log_fn(self._info if self._multi_env else self._info[0])
if render:
self.env.render()
if render > 0:
time.sleep(render)
self.length += 1
self.reward += self._rew
if self.preprocess_fn:
result = self.preprocess_fn(
obs=self._obs, act=self._act, rew=self._rew,
done=self._done, obs_next=obs_next, info=self._info,
policy=self._policy)
self._obs = result.get('obs', self._obs)
self._act = result.get('act', self._act)
self._rew = result.get('rew', self._rew)
self._done = result.get('done', self._done)
obs_next = result.get('obs_next', obs_next)
self._info = result.get('info', self._info)
self._policy = result.get('policy', self._policy)
if self._multi_env:
for i in range(self.env_num):
data = {
'obs': self._obs[i], 'act': self._act[i],
'rew': self._rew[i], 'done': self._done[i],
'obs_next': obs_next[i], 'info': self._info[i],
'policy': self._policy[i]}
if self._cached_buf:
warning_count += 1
self._cached_buf[i].add(**data)
elif self._multi_buf:
warning_count += 1
self.buffer[i].add(**data)
cur_step += 1
else:
warning_count += 1
if self.buffer is not None:
self.buffer.add(**data)
cur_step += 1
if self._done[i]:
if n_step != 0 or np.isscalar(n_episode) or \
cur_episode[i] < n_episode[i]:
cur_episode[i] += 1
reward_sum += self.reward[i]
length_sum += self.length[i]
if self._cached_buf:
cur_step += len(self._cached_buf[i])
if self.buffer is not None:
self.buffer.update(self._cached_buf[i])
self.reward[i], self.length[i] = 0, 0
if self._cached_buf:
self._cached_buf[i].reset()
self._reset_state(i)
if sum(self._done):
obs_next = self.env.reset(np.where(self._done)[0])
if self.preprocess_fn:
obs_next = self.preprocess_fn(obs=obs_next).get(
'obs', obs_next)
if n_episode != 0:
if isinstance(n_episode, list) and \
(cur_episode >= np.array(n_episode)).all() or \
np.isscalar(n_episode) and \
cur_episode.sum() >= n_episode:
break
else:
if self.buffer is not None:
self.buffer.add(
self._obs[0], self._act[0], self._rew[0],
self._done[0], obs_next[0], self._info[0],
self._policy[0])
cur_step += 1
if self._done:
cur_episode += 1
reward_sum += self.reward[0]
length_sum += self.length
self.reward, self.length = 0, 0
self.state = None
obs_next = self._make_batch(self.env.reset())
if self.preprocess_fn:
obs_next = self.preprocess_fn(obs=obs_next).get(
'obs', obs_next)
if n_episode != 0 and cur_episode >= n_episode:
break
if n_step != 0 and cur_step >= n_step:
break
self._obs = obs_next
self._obs = obs_next
if self._multi_env:
cur_episode = sum(cur_episode)
duration = max(time.time() - start_time, 1e-9)
self.step_speed.add(cur_step / duration)
self.episode_speed.add(cur_episode / duration)
self.collect_step += cur_step
self.collect_episode += cur_episode
self.collect_time += duration
if isinstance(n_episode, list):
n_episode = np.sum(n_episode)
else:
n_episode = max(cur_episode, 1)
return {
'n/ep': cur_episode,
'n/st': cur_step,
'v/st': self.step_speed.get(),
'v/ep': self.episode_speed.get(),
'rew': reward_sum / n_episode,
'len': length_sum / n_episode,
}
def sample(self, batch_size: int) -> Batch:
"""Sample a data batch from the internal replay buffer. It will call
:meth:`~tianshou.policy.BasePolicy.process_fn` before returning
the final batch data.
:param int batch_size: ``0`` means it will extract all the data from
the buffer, otherwise it will extract the data with the given
batch_size.
"""
if self._multi_buf:
if batch_size > 0:
lens = [len(b) for b in self.buffer]
total = sum(lens)
batch_index = np.random.choice(
len(self.buffer), batch_size, p=np.array(lens) / total)
else:
batch_index = np.array([])
batch_data = Batch()
for i, b in enumerate(self.buffer):
cur_batch = (batch_index == i).sum()
if batch_size and cur_batch or batch_size <= 0:
batch, indice = b.sample(cur_batch)
batch = self.process_fn(batch, b, indice)
batch_data.append(batch)
else:
batch_data, indice = self.buffer.sample(batch_size)
batch_data = self.process_fn(batch_data, self.buffer, indice)
return batch_data
class Collector(object):
"""The :class:`~tianshou.data.Collector` enables the policy to interact
with different types of environments conveniently.
:param policy: an instance of the :class:`~tianshou.policy.BasePolicy`
class.
:param env: a ``gym.Env`` environment or an instance of the
:class:`~tianshou.env.BaseVectorEnv` class.
:param buffer: an instance of the :class:`~tianshou.data.ReplayBuffer`
class, or a list of :class:`~tianshou.data.ReplayBuffer`. If set to
``None``, it will automatically assign a small-size
:class:`~tianshou.data.ReplayBuffer`.
:param function preprocess_fn: a function called before the data has been
added to the buffer, see issue #42 and :ref:`preprocess_fn`, defaults
to ``None``.
:param int stat_size: for the moving average of recording speed, defaults
to 100.
:param BaseNoise action_noise: add a noise to continuous action. Normally
a policy already has a noise param for exploration in training phase,
so this is recommended to use in test collector for some purpose.
:param function reward_metric: to be used in multi-agent RL. The reward to
report is of shape [agent_num], but we need to return a single scalar
to monitor training. This function specifies what is the desired
metric, e.g., the reward of agent 1 or the average reward over all
agents. By default, the behavior is to select the reward of agent 1.
The ``preprocess_fn`` is a function called before the data has been added
to the buffer with batch format, which receives up to 7 keys as listed in
:class:`~tianshou.data.Batch`. It will receive with only ``obs`` when the
collector resets the environment. It returns either a dict or a
:class:`~tianshou.data.Batch` with the modified keys and values. Examples
are in "test/base/test_collector.py".
Example:
::
policy = PGPolicy(...) # or other policies if you wish
env = gym.make('CartPole-v0')
replay_buffer = ReplayBuffer(size=10000)
# here we set up a collector with a single environment
collector = Collector(policy, env, buffer=replay_buffer)
# the collector supports vectorized environments as well
envs = VectorEnv([lambda: gym.make('CartPole-v0') for _ in range(3)])
buffers = [ReplayBuffer(size=5000) for _ in range(3)]
# you can also pass a list of replay buffer to collector, for multi-env
# collector = Collector(policy, envs, buffer=buffers)
collector = Collector(policy, envs, buffer=replay_buffer)
# collect at least 3 episodes
collector.collect(n_episode=3)
# collect 1 episode for the first env, 3 for the third env
collector.collect(n_episode=[1, 0, 3])
# collect at least 2 steps
collector.collect(n_step=2)
# collect episodes with visual rendering (the render argument is the
# sleep time between rendering consecutive frames)
collector.collect(n_episode=1, render=0.03)
# sample data with a given number of batch-size:
batch_data = collector.sample(batch_size=64)
# policy.learn(batch_data) # btw, vanilla policy gradient only
# supports on-policy training, so here we pick all data in the buffer
batch_data = collector.sample(batch_size=0)
policy.learn(batch_data)
# on-policy algorithms use the collected data only once, so here we
# clear the buffer
collector.reset_buffer()
For the scenario of collecting data from multiple environments to a single
buffer, the cache buffers will turn on automatically. It may return the
data more than the given limitation.
.. note::
Please make sure the given environment has a time limitation.
"""
def __init__(
self,
policy: BasePolicy,
env: Union[gym.Env, BaseVectorEnv],
buffer: Optional[ReplayBuffer] = None,
preprocess_fn: Callable[[Any], Union[dict, Batch]] = None,
stat_size: Optional[int] = 100,
action_noise: Optional[BaseNoise] = None,
reward_metric: Optional[Callable[[np.ndarray], float]] = None,
) -> None:
super().__init__()
self.env = env
self.env_num = 1
self.collect_time, self.collect_step, self.collect_episode = 0., 0, 0
self.buffer = buffer
self.policy = policy
self.preprocess_fn = preprocess_fn
self.process_fn = policy.process_fn
self._multi_env = isinstance(env, BaseVectorEnv)
# need multiple cache buffers only if storing in one buffer
self._cached_buf = []
if self._multi_env:
self.env_num = len(env)
self._cached_buf = [
ListReplayBuffer() for _ in range(self.env_num)
]
self.stat_size = stat_size
self._action_noise = action_noise
self._rew_metric = reward_metric or Collector._default_rew_metric
self.reset()
@staticmethod
def _default_rew_metric(x):
# this internal function is designed for single-agent RL
# for multi-agent RL, a reward_metric must be provided
assert np.asanyarray(x).size == 1, \
'Please specify the reward_metric ' \
'since the reward is not a scalar.'
return x
def reset(self) -> None:
"""Reset all related variables in the collector."""
self.data = Batch(state={},
obs={},
act={},
rew={},
done={},
info={},
obs_next={},
policy={})
self.reset_env()
self.reset_buffer()
self.step_speed = MovAvg(self.stat_size)
self.episode_speed = MovAvg(self.stat_size)
self.collect_time, self.collect_step, self.collect_episode = 0., 0, 0
if self._action_noise is not None:
self._action_noise.reset()
def reset_buffer(self) -> None:
"""Reset the main data buffer."""
if self.buffer is not None:
self.buffer.reset()
def get_env_num(self) -> int:
"""Return the number of environments the collector have."""
return self.env_num
def reset_env(self) -> None:
"""Reset all of the environment(s)' states and reset all of the cache
buffers (if need).
"""
obs = self.env.reset()
if not self._multi_env:
obs = self._make_batch(obs)
if self.preprocess_fn:
obs = self.preprocess_fn(obs=obs).get('obs', obs)
self.data.obs = obs
self.reward = 0. # will be specified when the first data is ready
self.length = np.zeros(self.env_num)
for b in self._cached_buf:
b.reset()
def seed(self, seed: Optional[Union[int, List[int]]] = None) -> None:
"""Reset all the seed(s) of the given environment(s)."""
return self.env.seed(seed)
def render(self, **kwargs) -> None:
"""Render all the environment(s)."""
return self.env.render(**kwargs)
def close(self) -> None:
"""Close the environment(s)."""
self.env.close()
def _make_batch(self, data: Any) -> np.ndarray:
"""Return [data]."""
if isinstance(data, np.ndarray):
return data[None]
else:
return np.array([data])
def _reset_state(self, id: Union[int, List[int]]) -> None:
"""Reset self.data.state[id]."""
state = self.data.state # it is a reference
if isinstance(state, torch.Tensor):
state[id].zero_()
elif isinstance(state, np.ndarray):
state[id] = None if state.dtype == np.object else 0
elif isinstance(state, Batch):
state.empty_(id)
def collect(
self,
n_step: int = 0,
n_episode: Union[int, List[int]] = 0,
random: bool = False,
render: Optional[float] = None,
log_fn: Optional[Callable[[dict],
None]] = None) -> Dict[str, float]:
"""Collect a specified number of step or episode.
:param int n_step: how many steps you want to collect.
:param n_episode: how many episodes you want to collect (in each
environment).
:type n_episode: int or list
:param bool random: whether to use random policy for collecting data,
defaults to ``False``.
:param float render: the sleep time between rendering consecutive
frames, defaults to ``None`` (no rendering).
:param function log_fn: a function which receives env info, typically
for tensorboard logging.
.. note::
One and only one collection number specification is permitted,
either ``n_step`` or ``n_episode``.
:return: A dict including the following keys
* ``n/ep`` the collected number of episodes.
* ``n/st`` the collected number of steps.
* ``v/st`` the speed of steps per second.
* ``v/ep`` the speed of episode per second.
* ``rew`` the mean reward over collected episodes.
* ``len`` the mean length over collected episodes.
"""
if not self._multi_env:
n_episode = np.sum(n_episode)
start_time = time.time()
assert sum([(n_step != 0), (n_episode != 0)]) == 1, \
"One and only one collection number specification is permitted!"
cur_step, cur_episode = 0, np.zeros(self.env_num)
reward_sum, length_sum = 0., 0
# change
ty1_succ_rate_1 = 0.
ty1_succ_rate_2 = 0.
ty1_succ_rate_3 = 0.
Q_len_1 = 0.
Q_len_2 = 0.
Q_len_3 = 0.
energy_effi_1 = 0.
energy_effi_2 = 0.
energy_effi_3 = 0.
avg_rate = 0.
avg_power = 0.
while True:
if cur_step >= 100000 and cur_episode.sum() == 0:
warnings.warn(
'There are already many steps in an episode. '
'You should add a time limitation to your environment!',
Warning)
# restore the state and the input data
last_state = self.data.state
if last_state.is_empty():
last_state = None
self.data.update(state=Batch(), obs_next=Batch(), policy=Batch())
# calculate the next action
if random:
action_space = self.env.action_space
if isinstance(action_space, list):
result = Batch(act=[a.sample() for a in action_space])
else:
result = Batch(act=self._make_batch(action_space.sample()))
else:
with torch.no_grad():
result = self.policy(self.data, last_state)
# convert None to Batch(), since None is reserved for 0-init
state = result.get('state', Batch())
if state is None:
state = Batch()
self.data.state = state
if hasattr(result, 'policy'):
self.data.policy = to_numpy(result.policy)
# save hidden state to policy._state, in order to save into buffer
self.data.policy._state = self.data.state
self.data.act = to_numpy(result.act)
if self._action_noise is not None:
self.data.act += self._action_noise(self.data.act.shape)
# step in env
obs_next, rew, done, info = self.env.step(
self.data.act if self._multi_env else self.data.act[0])
# move data to self.data
if not self._multi_env:
obs_next = self._make_batch(obs_next)
rew = self._make_batch(rew)
done = self._make_batch(done)
info = self._make_batch(info)
self.data.obs_next = obs_next
self.data.rew = rew
self.data.done = done
self.data.info = info
if log_fn:
log_fn(info if self._multi_env else info[0])
if render:
self.render()
if render > 0:
time.sleep(render)
# add data into the buffer
self.length += 1
self.reward += self.data.rew
if self.preprocess_fn:
result = self.preprocess_fn(**self.data)
self.data.update(result)
if self._multi_env: # cache_buffer branch
# change
if self.data.done[0]:
ty1_succ_rate_1 += self.data.info[0]['ty1_succ_rate_1']
ty1_succ_rate_2 += self.data.info[0]['ty1_succ_rate_2']
ty1_succ_rate_3 += self.data.info[0]['ty1_succ_rate_3']
Q_len_1 += self.data.info[0]['Q_len_1']
Q_len_2 += self.data.info[0]['Q_len_2']
Q_len_3 += self.data.info[0]['Q_len_3']
energy_effi_1 += self.data.info[0]['energy_effi_1']
energy_effi_2 += self.data.info[0]['energy_effi_2']
energy_effi_3 += self.data.info[0]['energy_effi_3']
avg_rate += self.data.info[0]['avg_rate']
avg_power += self.data.info[0]['avg_power']
for i in range(self.env_num):
self._cached_buf[i].add(**self.data[i])
if self.data.done[i]:
if n_step != 0 or np.isscalar(n_episode) or \
cur_episode[i] < n_episode[i]:
cur_episode[i] += 1
reward_sum += self.reward[i]
length_sum += self.length[i]
if self._cached_buf:
cur_step += len(self._cached_buf[i])
if self.buffer is not None:
self.buffer.update(self._cached_buf[i])
self.reward[i], self.length[i] = 0., 0
if self._cached_buf:
self._cached_buf[i].reset()
self._reset_state(i)
obs_next = self.data.obs_next
if sum(self.data.done):
env_ind = np.where(self.data.done)[0]
obs_reset = self.env.reset(env_ind)
if self.preprocess_fn:
obs_next[env_ind] = self.preprocess_fn(
obs=obs_reset).get('obs', obs_reset)
else:
obs_next[env_ind] = obs_reset
self.data.obs_next = obs_next
if n_episode != 0:
if isinstance(n_episode, list) and \
(cur_episode >= np.array(n_episode)).all() or \
np.isscalar(n_episode) and \
cur_episode.sum() >= n_episode:
break
else: # single buffer, without cache_buffer
if self.buffer is not None:
self.buffer.add(**self.data[0])
cur_step += 1
if self.data.done[0]:
# change
ty1_succ_rate_1 += self.data.info['ty1_succ_rate_1']
ty1_succ_rate_2 += self.data.info['ty1_succ_rate_2']
ty1_succ_rate_3 += self.data.info['ty1_succ_rate_3']
Q_len_1 += self.data.info['Q_len_1']
Q_len_2 += self.data.info['Q_len_2']
Q_len_3 += self.data.info['Q_len_3']
energy_effi_1 += self.data.info['energy_effi_1']
energy_effi_2 += self.data.info['energy_effi_2']
energy_effi_3 += self.data.info['energy_effi_3']
avg_rate += self.data.info[0]['avg_rate']
avg_power += self.data.info[0]['avg_power']
cur_episode += 1
reward_sum += self.reward[0]
length_sum += self.length[0]
self.reward, self.length = 0., np.zeros(self.env_num)
self.data.state = Batch()
obs_next = self._make_batch(self.env.reset())
if self.preprocess_fn:
obs_next = self.preprocess_fn(obs=obs_next).get(
'obs', obs_next)
self.data.obs_next = obs_next
if n_episode != 0 and cur_episode >= n_episode:
break
if n_step != 0 and cur_step >= n_step:
break
self.data.obs = self.data.obs_next
self.data.obs = self.data.obs_next
# generate the statistics
cur_episode = sum(cur_episode)
duration = max(time.time() - start_time, 1e-9)
self.step_speed.add(cur_step / duration)
self.episode_speed.add(cur_episode / duration)
self.collect_step += cur_step
self.collect_episode += cur_episode
self.collect_time += duration
if isinstance(n_episode, list):
n_episode = np.sum(n_episode)
else:
n_episode = max(cur_episode, 1)
reward_sum /= n_episode
if np.asanyarray(reward_sum).size > 1: # non-scalar reward_sum
reward_sum = self._rew_metric(reward_sum)
# change
return {
'n/ep': cur_episode,
'n/st': cur_step,
'v/st': self.step_speed.get(),
'v/ep': self.episode_speed.get(),
'rew': reward_sum,
'len': length_sum / n_episode,
'ty1s_1': ty1_succ_rate_1,
'ty1s_2': ty1_succ_rate_2,
'ty1s_3': ty1_succ_rate_3,
'ql_1': Q_len_1,
'ql_2': Q_len_2,
'ql_3': Q_len_3,
'ee_1': energy_effi_1,
'ee_2': energy_effi_2,
'ee_3': energy_effi_3,
'avg_r': avg_rate,
'avg_p': avg_power,
}
def sample(self, batch_size: int) -> Batch:
"""Sample a data batch from the internal replay buffer. It will call
:meth:`~tianshou.policy.BasePolicy.process_fn` before returning
the final batch data.
:param int batch_size: ``0`` means it will extract all the data from
the buffer, otherwise it will extract the data with the given
batch_size.
"""
batch_data, indice = self.buffer.sample(batch_size)
batch_data = self.process_fn(batch_data, self.buffer, indice)
return batch_data
class Collector(object):
"""docstring for Collector"""
def __init__(self, policy, env, buffer=None, stat_size=100):
super().__init__()
self.env = env
self.env_num = 1
self.collect_step = 0
self.collect_episode = 0
self.collect_time = 0
if buffer is None:
self.buffer = ReplayBuffer(100)
else:
self.buffer = buffer
self.policy = policy
self.process_fn = policy.process_fn
self._multi_env = isinstance(env, BaseVectorEnv)
self._multi_buf = False # True if buf is a list
# need multiple cache buffers only if storing in one buffer
self._cached_buf = []
if self._multi_env:
self.env_num = len(env)
if isinstance(self.buffer, list):
assert len(self.buffer) == self.env_num, \
'The number of data buffer does not match the number of ' \
'input env.'
self._multi_buf = True
elif isinstance(self.buffer, ReplayBuffer):
self._cached_buf = [
ListReplayBuffer() for _ in range(self.env_num)
]
else:
raise TypeError('The buffer in data collector is invalid!')
self.reset_env()
self.reset_buffer()
# state over batch is either a list, an np.ndarray, or a torch.Tensor
self.state = None
self.step_speed = MovAvg(stat_size)
self.episode_speed = MovAvg(stat_size)
def reset_buffer(self):
if self._multi_buf:
for b in self.buffer:
b.reset()
else:
self.buffer.reset()
def get_env_num(self):
return self.env_num
def reset_env(self):
self._obs = self.env.reset()
self._act = self._rew = self._done = self._info = None
if self._multi_env:
self.reward = np.zeros(self.env_num)
self.length = np.zeros(self.env_num)
else:
self.reward, self.length = 0, 0
for b in self._cached_buf:
b.reset()
def seed(self, seed=None):
if hasattr(self.env, 'seed'):
return self.env.seed(seed)
def render(self, **kwargs):
if hasattr(self.env, 'render'):
return self.env.render(**kwargs)
def close(self):
if hasattr(self.env, 'close'):
self.env.close()
def _make_batch(self, data):
if isinstance(data, np.ndarray):
return data[None]
else:
return np.array([data])
def collect(self, n_step=0, n_episode=0, render=0):
warning_count = 0
if not self._multi_env:
n_episode = np.sum(n_episode)
start_time = time.time()
assert sum([(n_step != 0), (n_episode != 0)]) == 1, \
"One and only one collection number specification permitted!"
cur_step = 0
cur_episode = np.zeros(self.env_num) if self._multi_env else 0
reward_sum = 0
length_sum = 0
while True:
if warning_count >= 100000:
warnings.warn(
'There are already many steps in an episode. '
'You should add a time limitation to your environment!',
Warning)
if self._multi_env:
batch_data = Batch(obs=self._obs,
act=self._act,
rew=self._rew,
done=self._done,
obs_next=None,
info=self._info)
else:
batch_data = Batch(obs=self._make_batch(self._obs),
act=self._make_batch(self._act),
rew=self._make_batch(self._rew),
done=self._make_batch(self._done),
obs_next=None,
info=self._make_batch(self._info))
result = self.policy(batch_data, self.state)
self.state = result.state if hasattr(result, 'state') else None
if isinstance(result.act, torch.Tensor):
self._act = result.act.detach().cpu().numpy()
elif not isinstance(self._act, np.ndarray):
self._act = np.array(result.act)
else:
self._act = result.act
obs_next, self._rew, self._done, self._info = self.env.step(
self._act if self._multi_env else self._act[0])
if render > 0:
self.env.render()
time.sleep(render)
self.length += 1
self.reward += self._rew
if self._multi_env:
for i in range(self.env_num):
data = {
'obs': self._obs[i],
'act': self._act[i],
'rew': self._rew[i],
'done': self._done[i],
'obs_next': obs_next[i],
'info': self._info[i]
}
if self._cached_buf:
warning_count += 1
self._cached_buf[i].add(**data)
elif self._multi_buf:
warning_count += 1
self.buffer[i].add(**data)
cur_step += 1
else:
warning_count += 1
self.buffer.add(**data)
cur_step += 1
if self._done[i]:
if n_step != 0 or np.isscalar(n_episode) or \
cur_episode[i] < n_episode[i]:
cur_episode[i] += 1
reward_sum += self.reward[i]
length_sum += self.length[i]
if self._cached_buf:
cur_step += len(self._cached_buf[i])
self.buffer.update(self._cached_buf[i])
self.reward[i], self.length[i] = 0, 0
if self._cached_buf:
self._cached_buf[i].reset()
if isinstance(self.state, list):
self.state[i] = None
elif self.state is not None:
if isinstance(self.state[i], dict):
self.state[i] = {}
else:
self.state[i] = self.state[i] * 0
if isinstance(self.state, torch.Tensor):
# remove ref count in pytorch (?)
self.state = self.state.detach()
if sum(self._done):
obs_next = self.env.reset(np.where(self._done)[0])
if n_episode != 0:
if isinstance(n_episode, list) and \
(cur_episode >= np.array(n_episode)).all() or \
np.isscalar(n_episode) and \
cur_episode.sum() >= n_episode:
break
else:
self.buffer.add(self._obs, self._act[0], self._rew, self._done,
obs_next, self._info)
cur_step += 1
if self._done:
cur_episode += 1
reward_sum += self.reward
length_sum += self.length
self.reward, self.length = 0, 0
self.state = None
obs_next = self.env.reset()
if n_episode != 0 and cur_episode >= n_episode:
break
if n_step != 0 and cur_step >= n_step:
break
self._obs = obs_next
self._obs = obs_next
if self._multi_env:
cur_episode = sum(cur_episode)
duration = time.time() - start_time
self.step_speed.add(cur_step / duration)
self.episode_speed.add(cur_episode / duration)
self.collect_step += cur_step
self.collect_episode += cur_episode
self.collect_time += duration
if isinstance(n_episode, list):
n_episode = np.sum(n_episode)
else:
n_episode = max(cur_episode, 1)
return {
'n/ep': cur_episode,
'n/st': cur_step,
'v/st': self.step_speed.get(),
'v/ep': self.episode_speed.get(),
'rew': reward_sum / n_episode,
'len': length_sum / n_episode,
}
def sample(self, batch_size):
if self._multi_buf:
if batch_size > 0:
lens = [len(b) for b in self.buffer]
total = sum(lens)
batch_index = np.random.choice(total,
batch_size,
p=np.array(lens) / total)
else:
batch_index = np.array([])
batch_data = Batch()
for i, b in enumerate(self.buffer):
cur_batch = (batch_index == i).sum()
if batch_size and cur_batch or batch_size <= 0:
batch, indice = b.sample(cur_batch)
batch = self.process_fn(batch, b, indice)
batch_data.append(batch)
else:
batch_data, indice = self.buffer.sample(batch_size)
batch_data = self.process_fn(batch_data, self.buffer, indice)
return batch_data
class Collector(object):
"""The :class:`~tianshou.data.Collector` enables the policy to interact
with different types of environments conveniently.
:param policy: an instance of the :class:`~tianshou.policy.BasePolicy`
class.
:param env: an environment or an instance of the
:class:`~tianshou.env.BaseVectorEnv` class.
:param buffer: an instance of the :class:`~tianshou.data.ReplayBuffer`
class, or a list of :class:`~tianshou.data.ReplayBuffer`. If set to
``None``, it will automatically assign a small-size
:class:`~tianshou.data.ReplayBuffer`.
:param int stat_size: for the moving average of recording speed, defaults
to 100.
:param bool store_obs_next: whether to store the obs_next to replay
buffer, defaults to ``True``.
Example:
::
policy = PGPolicy(...) # or other policies if you wish
env = gym.make('CartPole-v0')
replay_buffer = ReplayBuffer(size=10000)
# here we set up a collector with a single environment
collector = Collector(policy, env, buffer=replay_buffer)
# the collector supports vectorized environments as well
envs = VectorEnv([lambda: gym.make('CartPole-v0') for _ in range(3)])
buffers = [ReplayBuffer(size=5000) for _ in range(3)]
# you can also pass a list of replay buffer to collector, for multi-env
# collector = Collector(policy, envs, buffer=buffers)
collector = Collector(policy, envs, buffer=replay_buffer)
# collect at least 3 episodes
collector.collect(n_episode=3)
# collect 1 episode for the first env, 3 for the third env
collector.collect(n_episode=[1, 0, 3])
# collect at least 2 steps
collector.collect(n_step=2)
# collect episodes with visual rendering (the render argument is the
# sleep time between rendering consecutive frames)
collector.collect(n_episode=1, render=0.03)
# sample data with a given number of batch-size:
batch_data = collector.sample(batch_size=64)
# policy.learn(batch_data) # btw, vanilla policy gradient only
# supports on-policy training, so here we pick all data in the buffer
batch_data = collector.sample(batch_size=0)
policy.learn(batch_data)
# on-policy algorithms use the collected data only once, so here we
# clear the buffer
collector.reset_buffer()
For the scenario of collecting data from multiple environments to a single
buffer, the cache buffers will turn on automatically. It may return the
data more than the given limitation.
.. note::
Please make sure the given environment has a time limitation.
"""
def __init__(self, policy, env, buffer=None, stat_size=100,
store_obs_next=True, **kwargs):
super().__init__()
self.env = env
self.env_num = 1
self.collect_step = 0
self.collect_episode = 0
self.collect_time = 0
if buffer is None:
self.buffer = ReplayBuffer(100)
else:
self.buffer = buffer
self.policy = policy
self.process_fn = policy.process_fn
self._multi_env = isinstance(env, BaseVectorEnv)
self._multi_buf = False # True if buf is a list
# need multiple cache buffers only if storing in one buffer
self._cached_buf = []
if self._multi_env:
self.env_num = len(env)
if isinstance(self.buffer, list):
assert len(self.buffer) == self.env_num, \
'The number of data buffer does not match the number of ' \
'input env.'
self._multi_buf = True
elif isinstance(self.buffer, ReplayBuffer):
self._cached_buf = [
ListReplayBuffer() for _ in range(self.env_num)]
else:
raise TypeError('The buffer in data collector is invalid!')
self.reset_env()
self.reset_buffer()
# state over batch is either a list, an np.ndarray, or a torch.Tensor
self.state = None
self.step_speed = MovAvg(stat_size)
self.episode_speed = MovAvg(stat_size)
self._save_s_ = store_obs_next
def reset_buffer(self):
"""Reset the main data buffer."""
if self._multi_buf:
for b in self.buffer:
b.reset()
else:
self.buffer.reset()
def get_env_num(self):
"""Return the number of environments the collector has."""
return self.env_num
def reset_env(self):
"""Reset all of the environment(s)' states and reset all of the cache
buffers (if need).
"""
self._obs = self.env.reset()
self._act = self._rew = self._done = self._info = None
if self._multi_env:
self.reward = np.zeros(self.env_num)
self.length = np.zeros(self.env_num)
else:
self.reward, self.length = 0, 0
for b in self._cached_buf:
b.reset()
def seed(self, seed=None):
"""Reset all the seed(s) of the given environment(s)."""
if hasattr(self.env, 'seed'):
return self.env.seed(seed)
def render(self, **kwargs):
"""Render all the environment(s)."""
if hasattr(self.env, 'render'):
return self.env.render(**kwargs)
def close(self):
"""Close the environment(s)."""
if hasattr(self.env, 'close'):
self.env.close()
def _make_batch(self, data):
if isinstance(data, np.ndarray):
return data[None]
else:
return np.array([data])
def collect(self, n_step=0, n_episode=0, render=0):
"""Collect a specified number of step or episode.
:param int n_step: how many steps you want to collect.
:param n_episode: how many episodes you want to collect (in each
environment).
:type n_episode: int or list
:param float render: the sleep time between rendering consecutive
frames. No rendering if it is ``0`` (default option).
.. note::
One and only one collection number specification is permitted,
either ``n_step`` or ``n_episode``.
:return: A dict including the following keys
* ``n/ep`` the collected number of episodes.
* ``n/st`` the collected number of steps.
* ``v/st`` the speed of steps per second.
* ``v/ep`` the speed of episode per second.
* ``rew`` the mean reward over collected episodes.
* ``len`` the mean length over collected episodes.
"""
warning_count = 0
if not self._multi_env:
n_episode = np.sum(n_episode)
start_time = time.time()
assert sum([(n_step != 0), (n_episode != 0)]) == 1, \
"One and only one collection number specification is permitted!"
cur_step = 0
cur_episode = np.zeros(self.env_num) if self._multi_env else 0
reward_sum = 0
length_sum = 0
while True:
if warning_count >= 100000:
warnings.warn(
'There are already many steps in an episode. '
'You should add a time limitation to your environment!',
Warning)
if self._multi_env:
batch_data = Batch(
obs=self._obs, act=self._act, rew=self._rew,
done=self._done, obs_next=None, info=self._info)
else:
batch_data = Batch(
obs=self._make_batch(self._obs),
act=self._make_batch(self._act),
rew=self._make_batch(self._rew),
done=self._make_batch(self._done),
obs_next=None,
info=self._make_batch(self._info))
with torch.no_grad():
result = self.policy(batch_data, self.state)
self.state = result.state if hasattr(result, 'state') else None
if isinstance(result.act, torch.Tensor):
self._act = result.act.detach().cpu().numpy()
elif not isinstance(self._act, np.ndarray):
self._act = np.array(result.act)
else:
self._act = result.act
obs_next, self._rew, self._done, self._info = self.env.step(
self._act if self._multi_env else self._act[0])
if render > 0:
self.env.render()
time.sleep(render)
self.length += 1
self.reward += self._rew
if self._multi_env:
for i in range(self.env_num):
data = {
'obs': self._obs[i], 'act': self._act[i],
'rew': self._rew[i], 'done': self._done[i],
'obs_next': obs_next[i] if self._save_s_ else None,
'info': self._info[i]}
if self._cached_buf:
warning_count += 1
self._cached_buf[i].add(**data)
elif self._multi_buf:
warning_count += 1
self.buffer[i].add(**data)
cur_step += 1
else:
warning_count += 1
self.buffer.add(**data)
cur_step += 1
if self._done[i]:
if n_step != 0 or np.isscalar(n_episode) or \
cur_episode[i] < n_episode[i]:
cur_episode[i] += 1
reward_sum += self.reward[i]
length_sum += self.length[i]
if self._cached_buf:
cur_step += len(self._cached_buf[i])
self.buffer.update(self._cached_buf[i])
self.reward[i], self.length[i] = 0, 0
if self._cached_buf:
self._cached_buf[i].reset()
if isinstance(self.state, list):
self.state[i] = None
elif self.state is not None:
if isinstance(self.state[i], dict):
self.state[i] = {}
else:
self.state[i] = self.state[i] * 0
if isinstance(self.state, torch.Tensor):
# remove ref count in pytorch (?)
self.state = self.state.detach()
if sum(self._done):
obs_next = self.env.reset(np.where(self._done)[0])
if n_episode != 0:
if isinstance(n_episode, list) and \
(cur_episode >= np.array(n_episode)).all() or \
np.isscalar(n_episode) and \
cur_episode.sum() >= n_episode:
break
else:
self.buffer.add(
self._obs, self._act[0], self._rew,
self._done, obs_next if self._save_s_ else None,
self._info)
cur_step += 1
if self._done:
cur_episode += 1
reward_sum += self.reward
length_sum += self.length
self.reward, self.length = 0, 0
self.state = None
obs_next = self.env.reset()
if n_episode != 0 and cur_episode >= n_episode:
break
if n_step != 0 and cur_step >= n_step:
break
self._obs = obs_next
self._obs = obs_next
if self._multi_env:
cur_episode = sum(cur_episode)
duration = time.time() - start_time
self.step_speed.add(cur_step / duration)
self.episode_speed.add(cur_episode / duration)
self.collect_step += cur_step
self.collect_episode += cur_episode
self.collect_time += duration
if isinstance(n_episode, list):
n_episode = np.sum(n_episode)
else:
n_episode = max(cur_episode, 1)
return {
'n/ep': cur_episode,
'n/st': cur_step,
'v/st': self.step_speed.get(),
'v/ep': self.episode_speed.get(),
'rew': reward_sum / n_episode,
'len': length_sum / n_episode,
}
def sample(self, batch_size):
"""Sample a data batch from the internal replay buffer. It will call
:meth:`~tianshou.policy.BasePolicy.process_fn` before returning
the final batch data.
:param int batch_size: ``0`` means it will extract all the data from
the buffer, otherwise it will extract the data with the given
batch_size.
"""
if self._multi_buf:
if batch_size > 0:
lens = [len(b) for b in self.buffer]
total = sum(lens)
batch_index = np.random.choice(
total, batch_size, p=np.array(lens) / total)
else:
batch_index = np.array([])
batch_data = Batch()
for i, b in enumerate(self.buffer):
cur_batch = (batch_index == i).sum()
if batch_size and cur_batch or batch_size <= 0:
batch, indice = b.sample(cur_batch)
batch = self.process_fn(batch, b, indice)
batch_data.append(batch)
else:
batch_data, indice = self.buffer.sample(batch_size)
batch_data = self.process_fn(batch_data, self.buffer, indice)
return batch_data