import warnings
from dataclasses import dataclass
from typing import Any, Literal, TypeVar
import gymnasium as gym
import torch
import torch.nn.functional as F
from torch.distributions import kl_divergence
from tianshou.data import Batch, SequenceSummaryStats
from tianshou.policy import NPGPolicy
from tianshou.policy.base import TLearningRateScheduler
from tianshou.policy.modelfree.npg import NPGTrainingStats
from tianshou.policy.modelfree.pg import TDistributionFunction
[docs]
@dataclass(kw_only=True)
class TRPOTrainingStats(NPGTrainingStats):
step_size: SequenceSummaryStats
TTRPOTrainingStats = TypeVar("TTRPOTrainingStats", bound=TRPOTrainingStats)
[docs]
class TRPOPolicy(NPGPolicy[TTRPOTrainingStats]):
"""Implementation of Trust Region Policy Optimization. arXiv:1502.05477.
:param actor: the actor network following the rules in BasePolicy. (s -> logits)
:param critic: the critic network. (s -> V(s))
:param optim: the optimizer for actor and critic network.
:param dist_fn: distribution class for computing the action.
:param action_space: env's action space
:param max_kl: max kl-divergence used to constrain each actor network update.
:param backtrack_coeff: Coefficient to be multiplied by step size when
constraints are not met.
:param max_backtracks: Max number of backtracking times in linesearch.
:param optim_critic_iters: Number of times to optimize critic network per update.
:param actor_step_size: step size for actor update in natural gradient direction.
:param advantage_normalization: whether to do per mini-batch advantage
normalization.
:param gae_lambda: in [0, 1], param for Generalized Advantage Estimation.
:param max_batchsize: the maximum size of the batch when computing GAE.
:param discount_factor: in [0, 1].
:param reward_normalization: normalize estimated values to have std close to 1.
:param deterministic_eval: if True, use deterministic evaluation.
:param observation_space: the space of the observation.
: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].
:param lr_scheduler: if not None, will be called in `policy.update()`.
"""
def __init__(
self,
*,
actor: torch.nn.Module,
critic: torch.nn.Module,
optim: torch.optim.Optimizer,
dist_fn: TDistributionFunction,
action_space: gym.Space,
max_kl: float = 0.01,
backtrack_coeff: float = 0.8,
max_backtracks: int = 10,
optim_critic_iters: int = 5,
actor_step_size: float = 0.5,
advantage_normalization: bool = True,
gae_lambda: float = 0.95,
max_batchsize: int = 256,
discount_factor: float = 0.99,
# TODO: rename to return_normalization?
reward_normalization: bool = False,
deterministic_eval: bool = False,
observation_space: gym.Space | None = None,
action_scaling: bool = True,
action_bound_method: Literal["clip", "tanh"] | None = "clip",
lr_scheduler: TLearningRateScheduler | None = None,
) -> None:
super().__init__(
actor=actor,
critic=critic,
optim=optim,
dist_fn=dist_fn,
action_space=action_space,
optim_critic_iters=optim_critic_iters,
actor_step_size=actor_step_size,
advantage_normalization=advantage_normalization,
gae_lambda=gae_lambda,
max_batchsize=max_batchsize,
discount_factor=discount_factor,
reward_normalization=reward_normalization,
deterministic_eval=deterministic_eval,
observation_space=observation_space,
action_scaling=action_scaling,
action_bound_method=action_bound_method,
lr_scheduler=lr_scheduler,
)
self.max_backtracks = max_backtracks
self.max_kl = max_kl
self.backtrack_coeff = backtrack_coeff
[docs]
def learn( # type: ignore
self,
batch: Batch,
batch_size: int | None,
repeat: int,
**kwargs: Any,
) -> TTRPOTrainingStats:
actor_losses, vf_losses, step_sizes, kls = [], [], [], []
split_batch_size = batch_size or -1
for _ in range(repeat):
for minibatch in batch.split(split_batch_size, merge_last=True):
# optimize actor
# direction: calculate villia gradient
dist = self(minibatch).dist # TODO could come from batch
ratio = (dist.log_prob(minibatch.act) - minibatch.logp_old).exp().float()
ratio = ratio.reshape(ratio.size(0), -1).transpose(0, 1)
actor_loss = -(ratio * minibatch.adv).mean()
flat_grads = self._get_flat_grad(actor_loss, self.actor, retain_graph=True).detach()
# direction: calculate natural gradient
with torch.no_grad():
old_dist = self(minibatch).dist
kl = kl_divergence(old_dist, dist).mean()
# calculate first order gradient of kl with respect to theta
flat_kl_grad = self._get_flat_grad(kl, self.actor, create_graph=True)
search_direction = -self._conjugate_gradients(flat_grads, flat_kl_grad, nsteps=10)
# stepsize: calculate max stepsize constrained by kl bound
step_size = torch.sqrt(
2
* self.max_kl
/ (search_direction * self._MVP(search_direction, flat_kl_grad)).sum(
0,
keepdim=True,
),
)
# stepsize: linesearch stepsize
with torch.no_grad():
flat_params = torch.cat(
[param.data.view(-1) for param in self.actor.parameters()],
)
for i in range(self.max_backtracks):
new_flat_params = flat_params + step_size * search_direction
self._set_from_flat_params(self.actor, new_flat_params)
# calculate kl and if in bound, loss actually down
new_dist = self(minibatch).dist
new_dratio = (
(new_dist.log_prob(minibatch.act) - minibatch.logp_old).exp().float()
)
new_dratio = new_dratio.reshape(new_dratio.size(0), -1).transpose(0, 1)
new_actor_loss = -(new_dratio * minibatch.adv).mean()
kl = kl_divergence(old_dist, new_dist).mean()
if kl < self.max_kl and new_actor_loss < actor_loss:
if i > 0:
warnings.warn(f"Backtracking to step {i}.")
break
if i < self.max_backtracks - 1:
step_size = step_size * self.backtrack_coeff
else:
self._set_from_flat_params(self.actor, new_flat_params)
step_size = torch.tensor([0.0])
warnings.warn(
"Line search failed! It seems hyperparamters"
" are poor and need to be changed.",
)
# optimize critic
# TODO: remove type-ignore once the top-level type-ignore is removed
for _ in range(self.optim_critic_iters): # type: ignore
value = self.critic(minibatch.obs).flatten()
vf_loss = F.mse_loss(minibatch.returns, value)
self.optim.zero_grad()
vf_loss.backward()
self.optim.step()
actor_losses.append(actor_loss.item())
vf_losses.append(vf_loss.item())
step_sizes.append(step_size.item())
kls.append(kl.item())
actor_loss_summary_stat = SequenceSummaryStats.from_sequence(actor_losses)
vf_loss_summary_stat = SequenceSummaryStats.from_sequence(vf_losses)
kl_summary_stat = SequenceSummaryStats.from_sequence(kls)
step_size_stat = SequenceSummaryStats.from_sequence(step_sizes)
return TRPOTrainingStats( # type: ignore[return-value]
actor_loss=actor_loss_summary_stat,
vf_loss=vf_loss_summary_stat,
kl=kl_summary_stat,
step_size=step_size_stat,
)
