import copy
from dataclasses import dataclass
from typing import Any, Generic, Literal, Self, TypeVar, cast
import gymnasium as gym
import numpy as np
import torch
import torch.nn.functional as F
from tianshou.data import Batch, to_torch
from tianshou.data.batch import BatchProtocol
from tianshou.data.types import ActBatchProtocol, ObsBatchProtocol, RolloutBatchProtocol
from tianshou.policy import BasePolicy
from tianshou.policy.base import TLearningRateScheduler, TrainingStats
from tianshou.utils.net.continuous import VAE
from tianshou.utils.optim import clone_optimizer
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@dataclass(kw_only=True)
class BCQTrainingStats(TrainingStats):
actor_loss: float
critic1_loss: float
critic2_loss: float
vae_loss: float
TBCQTrainingStats = TypeVar("TBCQTrainingStats", bound=BCQTrainingStats)
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class BCQPolicy(BasePolicy[TBCQTrainingStats], Generic[TBCQTrainingStats]):
"""Implementation of BCQ algorithm. arXiv:1812.02900.
:param actor_perturbation: the actor perturbation. `(s, a -> perturbed a)`
:param actor_perturbation_optim: the optimizer for actor network.
:param critic: the first critic network.
:param critic_optim: the optimizer for the first critic network.
:param critic2: the second critic network.
:param critic2_optim: the optimizer for the second critic network.
:param vae: the VAE network, generating actions similar to those in batch.
:param vae_optim: the optimizer for the VAE network.
:param device: which device to create this model on.
:param gamma: discount factor, in [0, 1].
:param tau: param for soft update of the target network.
:param lmbda: param for Clipped Double Q-learning.
:param forward_sampled_times: the number of sampled actions in forward function.
The policy samples many actions and takes the action with the max value.
:param num_sampled_action: the number of sampled actions in calculating target Q.
The algorithm samples several actions using VAE, and perturbs each action to get the target Q.
:param observation_space: Env's observation space.
:param action_scaling: if True, scale the action from [-1, 1] to the range
of action_space. Only used if the action_space is continuous.
:param action_bound_method: method to bound action to range [-1, 1].
Only used if the action_space is continuous.
:param lr_scheduler: if not None, will be called in `policy.update()`.
.. seealso::
Please refer to :class:`~tianshou.policy.BasePolicy` for more detailed explanation.
"""
def __init__(
self,
*,
actor_perturbation: torch.nn.Module,
actor_perturbation_optim: torch.optim.Optimizer,
critic: torch.nn.Module,
critic_optim: torch.optim.Optimizer,
action_space: gym.Space,
vae: VAE,
vae_optim: torch.optim.Optimizer,
critic2: torch.nn.Module | None = None,
critic2_optim: torch.optim.Optimizer | None = None,
# TODO: remove? Many policies don't use this
device: str | torch.device = "cpu",
gamma: float = 0.99,
tau: float = 0.005,
lmbda: float = 0.75,
forward_sampled_times: int = 100,
num_sampled_action: int = 10,
observation_space: gym.Space | None = None,
action_scaling: bool = False,
action_bound_method: Literal["clip", "tanh"] | None = "clip",
lr_scheduler: TLearningRateScheduler | None = None,
) -> None:
# actor is Perturbation!
super().__init__(
action_space=action_space,
observation_space=observation_space,
action_scaling=action_scaling,
action_bound_method=action_bound_method,
lr_scheduler=lr_scheduler,
)
self.actor_perturbation = actor_perturbation
self.actor_perturbation_target = copy.deepcopy(self.actor_perturbation)
self.actor_perturbation_optim = actor_perturbation_optim
self.critic = critic
self.critic_target = copy.deepcopy(self.critic)
self.critic_optim = critic_optim
critic2 = critic2 or copy.deepcopy(critic)
critic2_optim = critic2_optim or clone_optimizer(critic_optim, critic2.parameters())
self.critic2 = critic2
self.critic2_target = copy.deepcopy(self.critic2)
self.critic2_optim = critic2_optim
self.vae = vae
self.vae_optim = vae_optim
self.gamma = gamma
self.tau = tau
self.lmbda = lmbda
self.device = device
self.forward_sampled_times = forward_sampled_times
self.num_sampled_action = num_sampled_action
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def train(self, mode: bool = True) -> Self:
"""Set the module in training mode, except for the target network."""
self.training = mode
self.actor_perturbation.train(mode)
self.critic.train(mode)
self.critic2.train(mode)
return self
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def forward(
self,
batch: ObsBatchProtocol,
state: dict | BatchProtocol | np.ndarray | None = None,
**kwargs: Any,
) -> ActBatchProtocol:
"""Compute action over the given batch data."""
# There is "obs" in the Batch
# obs_group: several groups. Each group has a state.
obs_group: torch.Tensor = to_torch(batch.obs, device=self.device)
act_group = []
for obs_orig in obs_group:
# now obs is (state_dim)
obs = (obs_orig.reshape(1, -1)).repeat(self.forward_sampled_times, 1)
# now obs is (forward_sampled_times, state_dim)
# decode(obs) generates action and actor perturbs it
act = self.actor_perturbation(obs, self.vae.decode(obs))
# now action is (forward_sampled_times, action_dim)
q1 = self.critic(obs, act)
# q1 is (forward_sampled_times, 1)
max_indice = q1.argmax(0)
act_group.append(act[max_indice].cpu().data.numpy().flatten())
act_group = np.array(act_group)
return cast(ActBatchProtocol, Batch(act=act_group))
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def sync_weight(self) -> None:
"""Soft-update the weight for the target network."""
self.soft_update(self.critic_target, self.critic, self.tau)
self.soft_update(self.critic2_target, self.critic2, self.tau)
self.soft_update(self.actor_perturbation_target, self.actor_perturbation, self.tau)
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def learn(self, batch: RolloutBatchProtocol, *args: Any, **kwargs: Any) -> TBCQTrainingStats:
# batch: obs, act, rew, done, obs_next. (numpy array)
# (batch_size, state_dim)
batch: Batch = to_torch(batch, dtype=torch.float, device=self.device)
obs, act = batch.obs, batch.act
batch_size = obs.shape[0]
# mean, std: (state.shape[0], latent_dim)
recon, mean, std = self.vae(obs, act)
recon_loss = F.mse_loss(act, recon)
# (....) is D_KL( N(mu, sigma) || N(0,1) )
KL_loss = (-torch.log(std) + (std.pow(2) + mean.pow(2) - 1) / 2).mean()
vae_loss = recon_loss + KL_loss / 2
self.vae_optim.zero_grad()
vae_loss.backward()
self.vae_optim.step()
# critic training:
with torch.no_grad():
# repeat num_sampled_action times
obs_next = batch.obs_next.repeat_interleave(self.num_sampled_action, dim=0)
# now obs_next: (num_sampled_action * batch_size, state_dim)
# perturbed action generated by VAE
act_next = self.vae.decode(obs_next)
# now obs_next: (num_sampled_action * batch_size, action_dim)
target_Q1 = self.critic_target(obs_next, act_next)
target_Q2 = self.critic2_target(obs_next, act_next)
# Clipped Double Q-learning
target_Q = self.lmbda * torch.min(target_Q1, target_Q2) + (1 - self.lmbda) * torch.max(
target_Q1,
target_Q2,
)
# now target_Q: (num_sampled_action * batch_size, 1)
# the max value of Q
target_Q = target_Q.reshape(batch_size, -1).max(dim=1)[0].reshape(-1, 1)
# now target_Q: (batch_size, 1)
target_Q = (
batch.rew.reshape(-1, 1) + (1 - batch.done).reshape(-1, 1) * self.gamma * target_Q
)
current_Q1 = self.critic(obs, act)
current_Q2 = self.critic2(obs, act)
critic1_loss = F.mse_loss(current_Q1, target_Q)
critic2_loss = F.mse_loss(current_Q2, target_Q)
self.critic_optim.zero_grad()
self.critic2_optim.zero_grad()
critic1_loss.backward()
critic2_loss.backward()
self.critic_optim.step()
self.critic2_optim.step()
sampled_act = self.vae.decode(obs)
perturbed_act = self.actor_perturbation(obs, sampled_act)
# max
actor_loss = -self.critic(obs, perturbed_act).mean()
self.actor_perturbation_optim.zero_grad()
actor_loss.backward()
self.actor_perturbation_optim.step()
# update target network
self.sync_weight()
return BCQTrainingStats( # type: ignore
actor_loss=actor_loss.item(),
critic1_loss=critic1_loss.item(),
critic2_loss=critic2_loss.item(),
vae_loss=vae_loss.item(),
)