import torch
import numpy as np
from torch import nn
from abc import ABC, abstractmethod
from typing import Dict, List, Union, Optional, Callable
from tianshou.data import Batch, ReplayBuffer, to_torch_as
[docs]class BasePolicy(ABC, nn.Module):
"""Tianshou aims to modularizing RL algorithms. It comes into several
classes of policies in Tianshou. All of the policy classes must inherit
:class:`~tianshou.policy.BasePolicy`.
A policy class typically has four parts:
* :meth:`~tianshou.policy.BasePolicy.__init__`: initialize the policy, \
including coping the target network and so on;
* :meth:`~tianshou.policy.BasePolicy.forward`: compute action with given \
observation;
* :meth:`~tianshou.policy.BasePolicy.process_fn`: pre-process data from \
the replay buffer (this function can interact with replay buffer);
* :meth:`~tianshou.policy.BasePolicy.learn`: update policy with a given \
batch of data.
Most of the policy needs a neural network to predict the action and an
optimizer to optimize the policy. The rules of self-defined networks are:
1. Input: observation ``obs`` (may be a ``numpy.ndarray``, a \
``torch.Tensor``, a dict or any others), hidden state ``state`` (for \
RNN usage), and other information ``info`` provided by the \
environment.
2. Output: some ``logits``, the next hidden state ``state``, and the \
intermediate result during policy forwarding procedure ``policy``. The\
``logits`` could be a tuple instead of a ``torch.Tensor``. It depends \
on how the policy process the network output. For example, in PPO, the\
return of the network might be ``(mu, sigma), state`` for Gaussian \
policy. The ``policy`` can be a Batch of torch.Tensor or other things,\
which will be stored in the replay buffer, and can be accessed in the \
policy update process (e.g. in ``policy.learn()``, the \
``batch.policy`` is what you need).
Since :class:`~tianshou.policy.BasePolicy` inherits ``torch.nn.Module``,
you can use :class:`~tianshou.policy.BasePolicy` almost the same as
``torch.nn.Module``, for instance, loading and saving the model:
::
torch.save(policy.state_dict(), 'policy.pth')
policy.load_state_dict(torch.load('policy.pth'))
"""
def __init__(self, **kwargs) -> None:
super().__init__()
self.observation_space = kwargs.get('observation_space')
self.action_space = kwargs.get('action_space')
[docs] def process_fn(self, batch: Batch, buffer: ReplayBuffer,
indice: np.ndarray) -> Batch:
"""Pre-process the data from the provided replay buffer. Check out
:ref:`policy_concept` for more information.
"""
return batch
[docs] @abstractmethod
def forward(self, batch: Batch,
state: Optional[Union[dict, Batch, np.ndarray]] = None,
**kwargs) -> Batch:
"""Compute action over the given batch data.
:return: A :class:`~tianshou.data.Batch` which MUST have the following\
keys:
* ``act`` an numpy.ndarray or a torch.Tensor, the action over \
given batch data.
* ``state`` a dict, an numpy.ndarray or a torch.Tensor, the \
internal state of the policy, ``None`` as default.
Other keys are user-defined. It depends on the algorithm. For example,
::
# some code
return Batch(logits=..., act=..., state=None, dist=...)
After version >= 0.2.3, the keyword "policy" is reserverd and the
corresponding data will be stored into the replay buffer in numpy. For
instance,
::
# some code
return Batch(..., policy=Batch(log_prob=dist.log_prob(act)))
# and in the sampled data batch, you can directly call
# batch.policy.log_prob to get your data, although it is stored in
# np.ndarray.
"""
pass
[docs] @abstractmethod
def learn(self, batch: Batch, **kwargs
) -> Dict[str, Union[float, List[float]]]:
"""Update policy with a given batch of data.
:return: A dict which includes loss and its corresponding label.
"""
pass
[docs] @staticmethod
def compute_episodic_return(
batch: Batch,
v_s_: Optional[Union[np.ndarray, torch.Tensor]] = None,
gamma: float = 0.99,
gae_lambda: float = 0.95) -> Batch:
"""Compute returns over given full-length episodes, including the
implementation of Generalized Advantage Estimator (arXiv:1506.02438).
:param batch: a data batch which contains several full-episode data
chronologically.
:type batch: :class:`~tianshou.data.Batch`
:param v_s_: the value function of all next states :math:`V(s')`.
:type v_s_: numpy.ndarray
:param float gamma: the discount factor, should be in [0, 1], defaults
to 0.99.
:param float gae_lambda: the parameter for Generalized Advantage
Estimation, should be in [0, 1], defaults to 0.95.
:return: a Batch. The result will be stored in batch.returns.
"""
if v_s_ is None:
v_s_ = batch.rew * 0.
else:
if not isinstance(v_s_, np.ndarray):
v_s_ = np.array(v_s_, np.float)
v_s_ = v_s_.reshape(batch.rew.shape)
returns = np.roll(v_s_, 1, axis=0)
m = (1. - batch.done) * gamma
delta = batch.rew + v_s_ * m - returns
m *= gae_lambda
gae = 0.
for i in range(len(batch.rew) - 1, -1, -1):
gae = delta[i] + m[i] * gae
returns[i] += gae
batch.returns = returns
return batch
[docs] @staticmethod
def compute_nstep_return(
batch: Batch,
buffer: ReplayBuffer,
indice: np.ndarray,
target_q_fn: Callable[[ReplayBuffer, np.ndarray], torch.Tensor],
gamma: float = 0.99,
n_step: int = 1,
rew_norm: bool = False
) -> np.ndarray:
r"""Compute n-step return for Q-learning targets:
.. math::
G_t = \sum_{i = t}^{t + n - 1} \gamma^{i - t}(1 - d_i)r_i +
\gamma^n (1 - d_{t + n}) Q_{\mathrm{target}}(s_{t + n})
, where :math:`\gamma` is the discount factor,
:math:`\gamma \in [0, 1]`, :math:`d_t` is the done flag of step
:math:`t`.
:param batch: a data batch, which is equal to buffer[indice].
:type batch: :class:`~tianshou.data.Batch`
:param buffer: a data buffer which contains several full-episode data
chronologically.
:type buffer: :class:`~tianshou.data.ReplayBuffer`
:param indice: sampled timestep.
:type indice: numpy.ndarray
:param function target_q_fn: a function receives :math:`t+n-1` step's
data and compute target Q value.
:param float gamma: the discount factor, should be in [0, 1], defaults
to 0.99.
:param int n_step: the number of estimation step, should be an int
greater than 0, defaults to 1.
:param bool rew_norm: normalize the reward to Normal(0, 1), defaults
to ``False``.
:return: a Batch. The result will be stored in batch.returns as a
torch.Tensor with shape (bsz, ).
"""
if rew_norm:
bfr = buffer.rew[:min(len(buffer), 1000)] # avoid large buffer
mean, std = bfr.mean(), bfr.std()
if np.isclose(std, 0):
mean, std = 0, 1
else:
mean, std = 0, 1
returns = np.zeros_like(indice)
gammas = np.zeros_like(indice) + n_step
done, rew, buf_len = buffer.done, buffer.rew, len(buffer)
for n in range(n_step - 1, -1, -1):
now = (indice + n) % buf_len
gammas[done[now] > 0] = n
returns[done[now] > 0] = 0
returns = (rew[now] - mean) / std + gamma * returns
terminal = (indice + n_step - 1) % buf_len
target_q = target_q_fn(buffer, terminal).squeeze()
target_q[gammas != n_step] = 0
returns = to_torch_as(returns, target_q)
gammas = to_torch_as(gamma ** gammas, target_q)
batch.returns = target_q * gammas + returns
return batch