import numpy as np
from argparse import Namespace
from operator import itemgetter
from gymnasium.spaces import Space
from xuance.common import List, Optional, MultiAgentBaseCallback
from xuance.environment import DummyVecMultiAgentEnv, SubprocVecMultiAgentEnv
from xuance.mindspore import Tensor, Module
from xuance.mindspore.utils import NormalizeFunctions, ActivationFunctions, InitializeFunctions
from xuance.mindspore.policies import REGISTRY_Policy
from xuance.mindspore.agents.multi_agent_rl.ippo_agents import IPPO_Agents
[docs]
class MAPPO_Agents(IPPO_Agents):
"""The implementation of MAPPO 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(MAPPO_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 (torch.nn.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]
# build representations
A_representation = self._build_representation(self.config.representation, self.observation_space, self.config)
if self.use_global_state:
dim_obs_all = sum(self.state_space.shape)
else:
dim_obs_all = sum([sum(self.observation_space[k].shape) for k in self.agent_keys])
space_critic_in = {k: (dim_obs_all,) 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 == "Categorical_MAAC_Policy":
policy = REGISTRY_Policy["Categorical_MAAC_Policy"](
action_space=self.action_space, n_agents=self.n_agents,
representation_actor=A_representation, representation_critic=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,
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 = False
elif self.config.policy == "Gaussian_MAAC_Policy":
policy = REGISTRY_Policy["Gaussian_MAAC_Policy"](
action_space=self.action_space, n_agents=self.n_agents,
representation_actor=A_representation, representation_critic=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"MAPPO currently does not support the policy named {self.config.policy}.")
return policy
def _build_critic_inputs(self, batch_size: int, obs_batch: dict,
state: Optional[np.ndarray]):
"""
Build inputs for critic representations before calculating actions.
Parameters:
batch_size (int): The size of the obs batch.
obs_batch (dict): Observations for each agent in self.agent_keys.
state (Optional[np.ndarray]): The global state.
Returns:
critic_input: The represented observations.
"""
if self.use_global_state:
if self.use_parameter_sharing:
key = self.model_keys[0]
bs = batch_size * self.n_agents
state_n = np.stack([state for _ in range(self.n_agents)], axis=1).reshape([bs, -1])
critic_input = {key: Tensor(state_n)}
else:
critic_input = {k: Tensor(state) for k in self.model_keys}
else:
if self.use_parameter_sharing:
key = self.model_keys[0]
bs = batch_size * self.n_agents
joint_obs = obs_batch[key].reshape([batch_size, self.n_agents, -1]).reshape([batch_size, 1, -1])
joint_obs = np.repeat(joint_obs, repeats=self.n_agents, axis=1)
else:
bs = batch_size
joint_obs = np.stack(itemgetter(*self.agent_keys)(obs_batch), axis=1)
joint_obs = joint_obs.reshape([bs, 1, -1]) if self.use_rnn else joint_obs.reshape([bs, -1])
critic_input = {k: Tensor(joint_obs) for k in self.model_keys}
return critic_input
[docs]
def get_actions(self,
obs_dict: List[dict],
state: Optional[np.ndarray] = None,
avail_actions_dict: Optional[List[dict]] = None,
rnn_hidden_actor: Optional[dict] = None,
rnn_hidden_critic: Optional[dict] = None,
test_mode: Optional[bool] = False):
"""
Returns actions for agents.
Parameters:
obs_dict (dict): Observations for each agent in self.agent_keys.
state (Optional[np.ndarray]): The global state.
avail_actions_dict (Optional[List[dict]]): Actions mask values, default is None.
rnn_hidden_actor (Optional[dict]): The RNN hidden states of actor representation.
rnn_hidden_critic (Optional[dict]): The RNN hidden states of critic representation.
test_mode (Optional[bool]): True for testing without noises.
Returns:
rnn_hidden_actor_new (dict): The new RNN hidden states of actor representation (if self.use_rnn=True).
rnn_hidden_critic_new (dict): The new RNN hidden states of critic representation (if self.use_rnn=True).
actions_dict (dict): The output actions.
log_pi_a (dict): The log of pi.
values_dict (dict): The evaluated critic values (when test_mode is False).
"""
n_env = len(obs_dict)
rnn_hidden_critic_new, values_out, log_pi_a_dict, values_dict = {}, {}, {}, {}
obs_input, agents_id, avail_actions_input = self._build_inputs(obs_dict, avail_actions_dict)
if self.continuous_control:
rnn_hidden_actor_new, pi_mu, pi_std = self.policy(observation=obs_input,
agent_ids=agents_id,
avail_actions=avail_actions_input,
rnn_hidden=rnn_hidden_actor)
else:
rnn_hidden_actor_new, pi_logits = self.policy(observation=obs_input,
agent_ids=agents_id,
avail_actions=avail_actions_input,
rnn_hidden=rnn_hidden_actor)
if not test_mode:
critic_input = self._build_critic_inputs(batch_size=n_env, obs_batch=obs_input, state=state)
rnn_hidden_critic_new, values_out = self.policy.get_values(observation=critic_input,
agent_ids=agents_id,
rnn_hidden=rnn_hidden_critic)
if self.use_parameter_sharing:
key = self.agent_keys[0]
if self.continuous_control:
pi_dists = self.policy.actor[key].distribution(mu=pi_mu[key], std=pi_std[key])
actions_sample = pi_dists.stochastic_sample()
actions_out = actions_sample.asnumpy().reshape(n_env, self.n_agents, -1)
else:
pi_dists = self.policy.actor[key].distribution(logits=pi_logits[key])
actions_sample = pi_dists.stochastic_sample()
actions_out = actions_sample.asnumpy().reshape(n_env, self.n_agents)
actions_dict = [{k: actions_out[e, i] for i, k in enumerate(self.agent_keys)} for e in range(n_env)]
if not test_mode:
log_pi_a = pi_dists.log_prob(actions_sample).asnumpy()
log_pi_a = log_pi_a.reshape(n_env, self.n_agents)
log_pi_a_dict = {k: log_pi_a[:, i] for i, k in enumerate(self.agent_keys)}
values_out[key] = values_out[key].asnumpy().reshape(n_env, self.n_agents)
values_dict = {k: values_out[key][:, i] for i, k in enumerate(self.agent_keys)}
else:
if self.continuous_control:
pi_dists = {k: self.policy.actor[k].distribution(pi_mu[k], pi_std[k]) for k in self.agent_keys}
actions_sample = {k: pi_dists[k].stochastic_sample() for k in self.agent_keys}
actions_dict = [{k: actions_sample[k].asnumpy()[e].reshape([-1]) for k in self.agent_keys}
for e in range(n_env)]
else:
pi_dists = {k: self.policy.actor[k].distribution(logits=pi_logits[k]) for k in self.agent_keys}
actions_sample = {k: pi_dists[k].stochastic_sample() for k in self.agent_keys}
actions_dict = [{k: actions_sample[k].asnumpy()[e].reshape([]) for k in self.agent_keys}
for e in range(n_env)]
if not test_mode:
log_pi_a = {k: pi_dists[k].log_prob(actions_sample[k]).asnumpy() for k in self.agent_keys}
log_pi_a_dict = {k: log_pi_a[k].reshape([n_env]) for i, k in enumerate(self.agent_keys)}
values_dict = {k: values_out[k].asnumpy().reshape([n_env]) for k in self.agent_keys}
return {"rnn_hidden_actor": rnn_hidden_actor_new, "rnn_hidden_critic": rnn_hidden_critic_new,
"actions": actions_dict, "log_pi": log_pi_a_dict, "values": values_dict}
[docs]
def values_next(self,
i_env: int,
obs_dict: dict,
state: Optional[np.ndarray] = None,
rnn_hidden_critic: Optional[dict] = None):
"""
Returns critic values of one environment that finished an episode.
Parameters:
i_env (int): The index of environment.
obs_dict (dict): Observations for each agent in self.agent_keys.
state (Optional[np.ndarray]): The global state.
rnn_hidden_critic (Optional[dict]): The RNN hidden states of critic representation.
Returns:
rnn_hidden_critic_new (dict): The new RNN hidden states of critic representation (if self.use_rnn=True).
values_dict: The critic values.
"""
n_env = 1
rnn_hidden_critic_i = None
if self.use_parameter_sharing:
key = self.agent_keys[0]
batch_size = n_env * self.n_agents
if self.use_rnn:
hidden_item_index = np.arange(i_env * self.n_agents, (i_env + 1) * self.n_agents)
rnn_hidden_critic_i = {key: self.policy.critic_representation[key].get_hidden_item(
hidden_item_index, *rnn_hidden_critic[key])}
if self.use_global_state:
critic_input = Tensor(np.repeat(state.reshape([n_env, 1, -1]),
self.n_agents, axis=1).reshape([batch_size, 1, -1]))
else:
obs_array = np.array(itemgetter(*self.agent_keys)(obs_dict))
critic_input = Tensor(np.repeat(obs_array.reshape([n_env, 1, -1]),
self.n_agents, axis=1).reshape([batch_size, 1, -1]))
agents_id = np.eye(self.n_agents, dtype=np.float32)[None].repeat(n_env, 0).reshape(batch_size, -1)
else:
if self.use_global_state:
critic_input = Tensor(np.repeat(state.reshape([batch_size, -1]), self.n_agents, axis=1))
else:
obs_array = np.array([itemgetter(*self.agent_keys)(obs_dict)]).reshape([batch_size, -1])
critic_input = Tensor(np.repeat(obs_array, self.n_agents, axis=1).reshape(batch_size, -1))
agents_id = np.eye(self.n_agents, dtype=np.float32)[None].repeat(n_env, 0).reshape(batch_size, -1)
rnn_hidden_critic_new, values_out = self.policy.get_values(observation={key: critic_input},
agent_ids=Tensor(agents_id),
rnn_hidden=rnn_hidden_critic_i)
values_out = values_out[key].reshape(self.n_agents)
values_dict = {k: values_out[i].asnumpy() for i, k in enumerate(self.agent_keys)}
else:
if self.use_rnn:
rnn_hidden_critic_i = {k: self.policy.critic_representation[k].get_hidden_item(
[i_env, ], *rnn_hidden_critic[k]) for k in self.agent_keys}
joint_obs = np.stack(itemgetter(*self.agent_keys)(obs_dict), axis=0).reshape([n_env, 1, -1])
critic_input = {k: Tensor(joint_obs) for k in self.agent_keys}
else:
critic_input_array = np.concatenate([obs_dict[k].reshape(n_env, 1, -1) for k in self.agent_keys],
axis=1).reshape(n_env, -1)
if self.use_global_state:
critic_input_array = state.reshape([n_env, -1])
critic_input = {k: Tensor(critic_input_array) for k in self.agent_keys}
rnn_hidden_critic_new, values_out = self.policy.get_values(observation=critic_input,
rnn_hidden=rnn_hidden_critic_i)
values_dict = {k: values_out[k].asnumpy().reshape([]) for k in self.agent_keys}
return rnn_hidden_critic_new, values_dict