bf0d632108
Closes #917 ### Internal Improvements - Better variable names related to model outputs (logits, dist input etc.). #1032 - Improved typing for actors and critics, using Tianshou classes like `Actor`, `ActorProb`, etc., instead of just `nn.Module`. #1032 - Added interfaces for most `Actor` and `Critic` classes to enforce the presence of `forward` methods. #1032 - Simplified `PGPolicy` forward by unifying the `dist_fn` interface (see associated breaking change). #1032 - Use `.mode` of distribution instead of relying on knowledge of the distribution type. #1032 ### Breaking Changes - Changed interface of `dist_fn` in `PGPolicy` and all subclasses to take a single argument in both continuous and discrete cases. #1032 --------- Co-authored-by: Arnau Jimenez <arnau.jimenez@zeiss.com> Co-authored-by: Michael Panchenko <m.panchenko@appliedai.de>
200 lines
8.9 KiB
Python
200 lines
8.9 KiB
Python
import warnings
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from dataclasses import dataclass
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from typing import Any, Literal, TypeVar
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import gymnasium as gym
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import torch
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import torch.nn.functional as F
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from torch.distributions import kl_divergence
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from tianshou.data import Batch, SequenceSummaryStats
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from tianshou.policy import NPGPolicy
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from tianshou.policy.base import TLearningRateScheduler
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from tianshou.policy.modelfree.npg import NPGTrainingStats
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from tianshou.policy.modelfree.pg import TDistFnDiscrOrCont
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from tianshou.utils.net.continuous import ActorProb, Critic
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from tianshou.utils.net.discrete import Actor as DiscreteActor
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from tianshou.utils.net.discrete import Critic as DiscreteCritic
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@dataclass(kw_only=True)
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class TRPOTrainingStats(NPGTrainingStats):
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step_size: SequenceSummaryStats
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TTRPOTrainingStats = TypeVar("TTRPOTrainingStats", bound=TRPOTrainingStats)
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class TRPOPolicy(NPGPolicy[TTRPOTrainingStats]):
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"""Implementation of Trust Region Policy Optimization. arXiv:1502.05477.
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:param actor: the actor network following the rules:
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If `self.action_type == "discrete"`: (`s_B` ->`action_values_BA`).
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If `self.action_type == "continuous"`: (`s_B` -> `dist_input_BD`).
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:param critic: the critic network. (s -> V(s))
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:param optim: the optimizer for actor and critic network.
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:param dist_fn: distribution class for computing the action.
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:param action_space: env's action space
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:param max_kl: max kl-divergence used to constrain each actor network update.
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:param backtrack_coeff: Coefficient to be multiplied by step size when
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constraints are not met.
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:param max_backtracks: Max number of backtracking times in linesearch.
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:param optim_critic_iters: Number of times to optimize critic network per update.
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:param actor_step_size: step size for actor update in natural gradient direction.
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:param advantage_normalization: whether to do per mini-batch advantage
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normalization.
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:param gae_lambda: in [0, 1], param for Generalized Advantage Estimation.
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:param max_batchsize: the maximum size of the batch when computing GAE.
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:param discount_factor: in [0, 1].
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:param reward_normalization: normalize estimated values to have std close to 1.
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:param deterministic_eval: if True, use deterministic evaluation.
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:param observation_space: the space of the observation.
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:param action_scaling: if True, scale the action from [-1, 1] to the range of
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action_space. Only used if the action_space is continuous.
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:param action_bound_method: method to bound action to range [-1, 1].
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:param lr_scheduler: if not None, will be called in `policy.update()`.
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"""
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def __init__(
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self,
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*,
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actor: torch.nn.Module | ActorProb | DiscreteActor,
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critic: torch.nn.Module | Critic | DiscreteCritic,
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optim: torch.optim.Optimizer,
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dist_fn: TDistFnDiscrOrCont,
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action_space: gym.Space,
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max_kl: float = 0.01,
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backtrack_coeff: float = 0.8,
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max_backtracks: int = 10,
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optim_critic_iters: int = 5,
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actor_step_size: float = 0.5,
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advantage_normalization: bool = True,
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gae_lambda: float = 0.95,
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max_batchsize: int = 256,
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discount_factor: float = 0.99,
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# TODO: rename to return_normalization?
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reward_normalization: bool = False,
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deterministic_eval: bool = False,
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observation_space: gym.Space | None = None,
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action_scaling: bool = True,
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action_bound_method: Literal["clip", "tanh"] | None = "clip",
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lr_scheduler: TLearningRateScheduler | None = None,
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) -> None:
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super().__init__(
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actor=actor,
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critic=critic,
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optim=optim,
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dist_fn=dist_fn,
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action_space=action_space,
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optim_critic_iters=optim_critic_iters,
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actor_step_size=actor_step_size,
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advantage_normalization=advantage_normalization,
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gae_lambda=gae_lambda,
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max_batchsize=max_batchsize,
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discount_factor=discount_factor,
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reward_normalization=reward_normalization,
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deterministic_eval=deterministic_eval,
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observation_space=observation_space,
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action_scaling=action_scaling,
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action_bound_method=action_bound_method,
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lr_scheduler=lr_scheduler,
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)
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self.max_backtracks = max_backtracks
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self.max_kl = max_kl
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self.backtrack_coeff = backtrack_coeff
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def learn( # type: ignore
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self,
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batch: Batch,
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batch_size: int | None,
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repeat: int,
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**kwargs: Any,
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) -> TTRPOTrainingStats:
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actor_losses, vf_losses, step_sizes, kls = [], [], [], []
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split_batch_size = batch_size or -1
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for _ in range(repeat):
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for minibatch in batch.split(split_batch_size, merge_last=True):
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# optimize actor
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# direction: calculate villia gradient
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dist = self(minibatch).dist # TODO could come from batch
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ratio = (dist.log_prob(minibatch.act) - minibatch.logp_old).exp().float()
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ratio = ratio.reshape(ratio.size(0), -1).transpose(0, 1)
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actor_loss = -(ratio * minibatch.adv).mean()
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flat_grads = self._get_flat_grad(actor_loss, self.actor, retain_graph=True).detach()
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# direction: calculate natural gradient
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with torch.no_grad():
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old_dist = self(minibatch).dist
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kl = kl_divergence(old_dist, dist).mean()
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# calculate first order gradient of kl with respect to theta
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flat_kl_grad = self._get_flat_grad(kl, self.actor, create_graph=True)
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search_direction = -self._conjugate_gradients(flat_grads, flat_kl_grad, nsteps=10)
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# stepsize: calculate max stepsize constrained by kl bound
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step_size = torch.sqrt(
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2
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* self.max_kl
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/ (search_direction * self._MVP(search_direction, flat_kl_grad)).sum(
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0,
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keepdim=True,
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),
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)
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# stepsize: linesearch stepsize
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with torch.no_grad():
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flat_params = torch.cat(
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[param.data.view(-1) for param in self.actor.parameters()],
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)
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for i in range(self.max_backtracks):
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new_flat_params = flat_params + step_size * search_direction
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self._set_from_flat_params(self.actor, new_flat_params)
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# calculate kl and if in bound, loss actually down
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new_dist = self(minibatch).dist
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new_dratio = (
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(new_dist.log_prob(minibatch.act) - minibatch.logp_old).exp().float()
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)
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new_dratio = new_dratio.reshape(new_dratio.size(0), -1).transpose(0, 1)
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new_actor_loss = -(new_dratio * minibatch.adv).mean()
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kl = kl_divergence(old_dist, new_dist).mean()
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if kl < self.max_kl and new_actor_loss < actor_loss:
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if i > 0:
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warnings.warn(f"Backtracking to step {i}.")
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break
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if i < self.max_backtracks - 1:
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step_size = step_size * self.backtrack_coeff
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else:
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self._set_from_flat_params(self.actor, new_flat_params)
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step_size = torch.tensor([0.0])
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warnings.warn(
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"Line search failed! It seems hyperparamters"
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" are poor and need to be changed.",
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)
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# optimize critic
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# TODO: remove type-ignore once the top-level type-ignore is removed
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for _ in range(self.optim_critic_iters): # type: ignore
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value = self.critic(minibatch.obs).flatten()
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vf_loss = F.mse_loss(minibatch.returns, value)
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self.optim.zero_grad()
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vf_loss.backward()
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self.optim.step()
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actor_losses.append(actor_loss.item())
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vf_losses.append(vf_loss.item())
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step_sizes.append(step_size.item())
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kls.append(kl.item())
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actor_loss_summary_stat = SequenceSummaryStats.from_sequence(actor_losses)
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vf_loss_summary_stat = SequenceSummaryStats.from_sequence(vf_losses)
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kl_summary_stat = SequenceSummaryStats.from_sequence(kls)
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step_size_stat = SequenceSummaryStats.from_sequence(step_sizes)
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return TRPOTrainingStats( # type: ignore[return-value]
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actor_loss=actor_loss_summary_stat,
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vf_loss=vf_loss_summary_stat,
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kl=kl_summary_stat,
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step_size=step_size_stat,
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)
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