57 lines
2.1 KiB
Python
57 lines
2.1 KiB
Python
import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.optim as optim
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from rsl_rl.algorithms.ppo import PPO
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from rsl_rl.algorithms.tppo import TPPO
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from rsl_rl.utils.utils import unpad_trajectories, get_subobs_by_components
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from rsl_rl.storage.rollout_storage import SarsaRolloutStorage
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class EstimatorAlgoMixin:
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""" A supervised algorithm implementation that trains a state predictor in the policy model
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"""
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def __init__(self,
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*args,
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estimator_loss_func= "mse_loss",
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estimator_loss_kwargs= dict(),
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**kwargs,
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):
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super().__init__(*args, **kwargs)
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self.estimator_obs_components = self.actor_critic.estimator_obs_components
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self.estimator_target_obs_components = self.actor_critic.estimator_target_components
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self.estimator_loss_func = estimator_loss_func
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self.estimator_loss_kwargs = estimator_loss_kwargs
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def compute_losses(self, minibatch):
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losses, inter_vars, stats = super().compute_losses(minibatch)
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# Use the critic_obs from the same timestep for estimation target
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# Not considering predicting the next state for now.
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estimation_target = get_subobs_by_components(
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minibatch.critic_obs,
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component_names= self.estimator_target_obs_components,
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obs_segments= self.actor_critic.privileged_obs_segments,
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)
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if self.actor_critic.is_recurrent:
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estimation_target = unpad_trajectories(estimation_target, minibatch.masks)
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# actor_critic must compute the estimated_state_ during act() as a intermediate variable
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estimation = unpad_trajectories(self.actor_critic.get_estimated_state(), minibatch.masks)
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estimator_loss = getattr(F, self.estimator_loss_func)(
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estimation,
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estimation_target,
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**self.estimator_loss_kwargs,
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reduction= "none",
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).sum(dim= -1)
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losses["estimator_loss"] = estimator_loss.mean()
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return losses, inter_vars, stats
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class EstimatorPPO(EstimatorAlgoMixin, PPO):
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pass
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class EstimatorTPPO(EstimatorAlgoMixin, TPPO):
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pass |