Source code for xuance.tensorflow.agents.multi_agent_rl.masac_agents

from argparse import Namespace
from gymnasium.spaces import Space
from xuance.common import List, Optional, MultiAgentBaseCallback
from xuance.environment import DummyVecMultiAgentEnv, SubprocVecMultiAgentEnv
from xuance.tensorflow import Module
from xuance.tensorflow.utils import NormalizeFunctions, ActivationFunctions, InitializeFunctions
from xuance.tensorflow.policies import REGISTRY_Policy
from xuance.tensorflow.agents.multi_agent_rl.isac_agents import ISAC_Agents




class MASAC_Agents(ISAC_Agents):
    """The implementation of MASAC agents.

    Args:
        config: the Namespace variable that provides hyperparameters and other settings.
        envs: the vectorized environments.
        callback: A user-defined callback function object to inject custom logic during training.
    """
    def __init__(
            self,
            config: Namespace,
            envs: Optional[DummyVecMultiAgentEnv | SubprocVecMultiAgentEnv] = None,
            num_agents: Optional[int] = None,
            agent_keys: Optional[List[str]] = None,
            state_space: Optional[Space] = None,
            observation_space: Optional[Space] = None,
            action_space: Optional[Space] = None,
            callback: Optional[MultiAgentBaseCallback] = None
    ):
        super(MASAC_Agents, self).__init__(
            config, envs, num_agents, agent_keys, state_space, observation_space, action_space, callback
        )

    def _build_policy(self) -> Module:
        """
        Build representation(s) and policy(ies) for agent(s)

        Returns:
            policy (Module): A dict of policies.
        """
        normalize_fn = NormalizeFunctions[self.config.normalize] if hasattr(self.config, "normalize") else None
        initializer = InitializeFunctions[self.config.initialize] if hasattr(self.config, "initialize") else None
        activation = ActivationFunctions[self.config.activation]
        agent = self.config.agent

        # build representations
        A_representation = self._build_representation(self.config.representation, self.observation_space, self.config)
        critic_in = [sum(self.observation_space[k].shape) + sum(self.action_space[k].shape) for k in self.agent_keys]
        space_critic_in = {k: (sum(critic_in),) for k in self.agent_keys}
        C_representation = self._build_representation(self.config.representation, space_critic_in, self.config)

        # build policies
        if self.config.policy == "Gaussian_MASAC_Policy":
            policy = REGISTRY_Policy["Gaussian_MASAC_Policy"](
                action_space=self.action_space, n_agents=self.n_agents,
                actor_representation=A_representation, critic_representation=C_representation,
                actor_hidden_size=self.config.actor_hidden_size,
                critic_hidden_size=self.config.critic_hidden_size,
                normalize=normalize_fn, initialize=initializer, activation=activation,
                activation_action=ActivationFunctions[self.config.activation_action],
                use_parameter_sharing=self.use_parameter_sharing, model_keys=self.model_keys,
                use_rnn=self.use_rnn, rnn=self.config.rnn if self.use_rnn else None)
            self.continuous_control = True
        else:
            raise AttributeError(f"{agent} currently does not support the policy named {self.config.policy}.")

        return policy