Source code for omnisafe.models.actor_critic.constraint_actor_q_critic

# Copyright 2023 OmniSafe Team. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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# ==============================================================================
"""Implementation of ConstraintActorQCritic."""

from copy import deepcopy

from torch import optim

from omnisafe.models.actor_critic.actor_q_critic import ActorQCritic
from omnisafe.models.base import Critic
from omnisafe.models.critic.critic_builder import CriticBuilder
from omnisafe.typing import OmnisafeSpace
from omnisafe.utils.config import ModelConfig


class ConstraintActorQCritic(ActorQCritic):
    """ConstraintActorQCritic is a wrapper around ActorCritic that adds a cost critic to the model.

    In OmniSafe, we combine the actor and critic into one this class.

    +-----------------+---------------------------------------------------+
    | Model           | Description                                       |
    +=================+===================================================+
    | Actor           | Input is observation. Output is action.           |
    +-----------------+---------------------------------------------------+
    | Reward Q Critic | Input is obs-action pair, Output is reward value. |
    +-----------------+---------------------------------------------------+
    | Cost Q Critic   | Input is obs-action pair. Output is cost value.   |
    +-----------------+---------------------------------------------------+

    Args:
        obs_space (OmnisafeSpace): The observation space.
        act_space (OmnisafeSpace): The action space.
        model_cfgs (ModelConfig): The model configurations.
        epochs (int): The number of epochs.

    Attributes:
        actor (Actor): The actor network.
        target_actor (Actor): The target actor network.
        reward_critic (Critic): The critic network.
        target_reward_critic (Critic): The target critic network.
        cost_critic (Critic): The critic network.
        target_cost_critic (Critic): The target critic network.
        actor_optimizer (Optimizer): The optimizer for the actor network.
        reward_critic_optimizer (Optimizer): The optimizer for the critic network.
        std_schedule (Schedule): The schedule for the standard deviation of the Gaussian distribution.
    """

    def __init__(
        self,
        obs_space: OmnisafeSpace,
        act_space: OmnisafeSpace,
        model_cfgs: ModelConfig,
        epochs: int,
    ) -> None:
        """Initialize an instance of :class:`ConstraintActorQCritic`."""
        super().__init__(obs_space, act_space, model_cfgs, epochs)

        self.cost_critic: Critic = CriticBuilder(
            obs_space=obs_space,
            act_space=act_space,
            hidden_sizes=model_cfgs.critic.hidden_sizes,
            activation=model_cfgs.critic.activation,
            weight_initialization_mode=model_cfgs.weight_initialization_mode,
            num_critics=1,
            use_obs_encoder=False,
        ).build_critic('q')
        self.target_cost_critic: Critic = deepcopy(self.cost_critic)
        for param in self.target_cost_critic.parameters():
            param.requires_grad = False
        self.add_module('cost_critic', self.cost_critic)
        if model_cfgs.critic.lr is not None:
            self.cost_critic_optimizer: optim.Optimizer
            self.cost_critic_optimizer = optim.Adam(
                self.cost_critic.parameters(),
                lr=model_cfgs.critic.lr,
            )

    def polyak_update(self, tau: float) -> None:
        """Update the target network with polyak averaging.

        Args:
            tau (float): The polyak averaging factor.
        """
        super().polyak_update(tau)
        for target_param, param in zip(
            self.target_cost_critic.parameters(),
            self.cost_critic.parameters(),
        ):
            target_param.data.copy_(tau * param.data + (1 - tau) * target_param.data)