IPPO_Agents
=====================================

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    <br><hr>

PyTorch
------------------------------------------

.. py:class:: 
    xuance.torch.agent.mutli_agent_rl.ippo_agents.IPPO_Agents(config, envs, device)

    :param config: Provides hyper parameters.
    :type config: Namespace
    :param envs: The vectorized environments.
    :type envs: xuance.environments.vector_envs.vector_env.VecEnv
    :param device: Choose CPU or GPU to train the model.
    :type device: str, int, torch.device

.. py:function::
    xuance.torch.agent.mutli_agent_rl.ippo_agents.IPPO_Agents.act(obs_n, *rnn_hidden, avail_actions=None, state=None, test_mode=False)

    Calculate joint actions for N agents according to the joint observations.

    :param obs_n: The joint observations of N agents.
    :type obs_n: np.ndarray
    :param rnn_hidden: The hidden states of RNN.
    :type rnn_hidden: tuple(np.ndarray, np.ndarray)
    :param avail_actions: The actions mask for available actions in the environment.
    :type avail_actions: np.ndarray
    :param state: The global state of the environments.
    :type state: np.ndarray
    :param test_mode: is True for selecting greedy actions, is False for selecting epsilon-greedy actions.
    :type test_mode: bool
    :return: **hidden_state**, **actions_n** - The next hidden states of RNN and the joint actions.
    :rtype: tuple(np.ndarray, np.ndarray), np.ndarray

.. py:function::
    xuance.torch.agent.mutli_agent_rl.ippo_agents.IPPO_Agents.values(obs_n, *rnn_hidden, state=None)

    Get the estimated values of the actions for the given observations.

    :param obs_n: The joint observations of n agents.
    :type obs_n: np.ndarray
    :param rnn_hidden: The final hidden state of the sequence.
    :type rnn_hidden: tuple
    :param state: The state input.
    :type state: Tensor
    :return: the hidden state and the estimated values of the actions.
    :rtype: tuple

.. py:function::
    xuance.torch.agent.mutli_agent_rl.ippo_agents.IPPO_Agents.train(i_step, **kwargs)

    Train the multi-agent reinforcement learning model.

    :param i_step: The i-th step during training.
    :type i_step: int
    :param kwargs: The other arguments.
    :type kwargs: dict
    :return: **info_train** - the information of the training process.
    :rtype: dict

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    <br><hr>

TensorFlow
------------------------------------------

.. py:class::
    xuance.tensorflow.agent.mutli_agent_rl.ippo_agents.IPPO_Agents(config, envs, device)

    :param config: Provides hyper parameters.
    :type config: Namespace
    :param envs: The vectorized environments.
    :type envs: xuance.environments.vector_envs.vector_env.VecEnv
    :param device: Choose CPU or GPU to train the model.
    :type device: str, int, torch.device

.. py:function::
    xuance.tensorflow.agent.mutli_agent_rl.ippo_agents.IPPO_Agents.act(obs_n, *rnn_hidden, avail_actions=None, state=None, test_mode=False)

    Calculate joint actions for N agents according to the joint observations.

    :param obs_n: The joint observations of N agents.
    :type obs_n: np.ndarray
    :param rnn_hidden: The hidden states of RNN.
    :type rnn_hidden: tuple(np.ndarray, np.ndarray)
    :param avail_actions: The actions mask for available actions in the environment.
    :type avail_actions: np.ndarray
    :param state: The global state of the environments.
    :type state: np.ndarray
    :param test_mode: is True for selecting greedy actions, is False for selecting epsilon-greedy actions.
    :type test_mode: bool
    :return: **hidden_state**, **actions_n** - The next hidden states of RNN and the joint actions.
    :rtype: tuple(np.ndarray, np.ndarray), np.ndarray

.. py:function::
    xuance.tensorflow.agent.mutli_agent_rl.ippo_agents.IPPO_Agents.values(obs_n, *rnn_hidden, state=None)

    Get the estimated values of the actions for the given observations.

    :param obs_n: The joint observations of n agents.
    :type obs_n: np.ndarray
    :param rnn_hidden: The final hidden state of the sequence.
    :type rnn_hidden: tuple
    :param state: The state input.
    :type state: Tensor
    :return: the hidden state and the estimated values of the actions.
    :rtype: tuple

.. py:function::
    xuance.tensorflow.agent.mutli_agent_rl.ippo_agents.IPPO_Agents.train(i_step, **kwargs)

    Train the multi-agent reinforcement learning model.

    :param i_step: The i-th step during training.
    :type i_step: int
    :param kwargs: The other arguments.
    :type kwargs: dict
    :return: **info_train** - the information of the training process.
    :rtype: dict


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    <br><hr>

MindSpore
------------------------------------------

.. py:class::
    xuance.mindspore.agent.mutli_agent_rl.ippo_agents.IPPO_Agents(config, envs)

    :param config: Provides hyper parameters.
    :type config: Namespace
    :param envs: The vectorized environments.
    :type envs: xuance.environments.vector_envs.vector_env.VecEnv

.. py:function::
    xuance.mindspore.agent.mutli_agent_rl.ippo_agents.IPPO_Agents.act(obs_n, *rnn_hidden, avail_actions=None, state=None, test_mode=False)

    Calculate joint actions for N agents according to the joint observations.

    :param obs_n: The joint observations of N agents.
    :type obs_n: np.ndarray
    :param rnn_hidden: The hidden states of RNN.
    :type rnn_hidden: tuple(np.ndarray, np.ndarray)
    :param avail_actions: The actions mask for available actions in the environment.
    :type avail_actions: np.ndarray
    :param state: The global state of the environments.
    :type state: np.ndarray
    :param test_mode: is True for selecting greedy actions, is False for selecting epsilon-greedy actions.
    :type test_mode: bool
    :return: **hidden_state**, **actions_n** - The next hidden states of RNN and the joint actions.
    :rtype: tuple(np.ndarray, np.ndarray), np.ndarray

.. py:function::
    xuance.mindspore.agent.mutli_agent_rl.ippo_agents.IPPO_Agents.values(obs_n, *rnn_hidden, state=None)

    Get the estimated values of the actions for the given observations.

    :param obs_n: The joint observations of n agents.
    :type obs_n: np.ndarray
    :param rnn_hidden: The final hidden state of the sequence.
    :type rnn_hidden: tensor
    :param state: The state input.
    :type state: Tensor
    :return: the hidden state and the estimated values of the actions.
    :rtype: tuple

.. py:function::
    xuance.mindspore.agent.mutli_agent_rl.ippo_agents.IPPO_Agents.train(i_step, **kwargs)

    Train the multi-agent reinforcement learning model.

    :param i_step: The i-th step during training.
    :type i_step: int
    :param kwargs: The other arguments.
    :type kwargs: dict
    :return: **info_train** - the information of the training process.
    :rtype: dict

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    <br><hr>

Source Code
-----------------

.. tabs::
  
    .. group-tab:: PyTorch
    
        .. code-block:: python

            from xuance.torch.agents import *


            class IPPO_Agents(MARLAgents):
                """The implementation of Independent PPO agents.

                Args:
                    config: the Namespace variable that provides hyper-parameters and other settings.
                    envs: the vectorized environments.
                    device: the calculating device of the model, such as CPU or GPU.
                """
                def __init__(self,
                            config: Namespace,
                            envs: DummyVecEnv_Pettingzoo,
                            device: Optional[Union[int, str, torch.device]] = None):
                    self.gamma = config.gamma
                    self.n_envs = envs.num_envs
                    self.n_size = config.n_size
                    self.n_epoch = config.n_epoch
                    self.n_minibatch = config.n_minibatch
                    if config.state_space is not None:
                        config.dim_state, state_shape = config.state_space.shape[0], config.state_space.shape
                    else:
                        config.dim_state, state_shape = None, None

                    input_representation = get_repre_in(config)
                    self.use_recurrent = config.use_recurrent
                    self.use_global_state = config.use_global_state
                    # create representation for actor
                    kwargs_rnn = {"N_recurrent_layers": config.N_recurrent_layers,
                                "dropout": config.dropout,
                                "rnn": config.rnn} if self.use_recurrent else {}
                    representation = REGISTRY_Representation[config.representation](*input_representation, **kwargs_rnn)
                    # create representation for critic
                    input_representation[0] = (config.dim_state,) if self.use_global_state else (config.dim_obs,)
                    representation_critic = REGISTRY_Representation[config.representation](*input_representation, **kwargs_rnn)
                    # create policy
                    input_policy = get_policy_in_marl(config, (representation, representation_critic))
                    policy = REGISTRY_Policy[config.policy](*input_policy,
                                                            use_recurrent=config.use_recurrent,
                                                            rnn=config.rnn,
                                                            gain=config.gain)
                    optimizer = torch.optim.Adam(policy.parameters(),
                                                lr=config.learning_rate, eps=1e-5,
                                                weight_decay=config.weight_decay)
                    self.observation_space = envs.observation_space
                    self.action_space = envs.action_space
                    self.auxiliary_info_shape = {}

                    buffer = MARL_OnPolicyBuffer_RNN if self.use_recurrent else MARL_OnPolicyBuffer
                    input_buffer = (config.n_agents, config.state_space.shape, config.obs_shape, config.act_shape, config.rew_shape,
                                    config.done_shape, envs.num_envs, config.n_size,
                                    config.use_gae, config.use_advnorm, config.gamma, config.gae_lambda)
                    memory = buffer(*input_buffer, max_episode_length=envs.max_episode_length, dim_act=config.dim_act)
                    self.buffer_size = memory.buffer_size
                    self.batch_size = self.buffer_size // self.n_minibatch

                    learner = IPPO_Learner(config, policy, optimizer, None, config.device, config.model_dir, config.gamma)
                    super(IPPO_Agents, self).__init__(config, envs, policy, memory, learner, device,
                                                    config.log_dir, config.model_dir)
                    self.share_values = True if config.rew_shape[0] == 1 else False
                    self.on_policy = True

                def act(self, obs_n, *rnn_hidden, avail_actions=None, state=None, test_mode=False):
                    batch_size = len(obs_n)
                    agents_id = torch.eye(self.n_agents).unsqueeze(0).expand(batch_size, -1, -1).to(self.device)
                    obs_in = torch.Tensor(obs_n).view([batch_size, self.n_agents, -1]).to(self.device)
                    if self.use_recurrent:
                        batch_agents = batch_size * self.n_agents
                        hidden_state, dists = self.policy(obs_in.view(batch_agents, 1, -1),
                                                        agents_id.view(batch_agents, 1, -1),
                                                        *rnn_hidden,
                                                        avail_actions=avail_actions.reshape(batch_agents, 1, -1))
                        actions = dists.stochastic_sample()
                        log_pi_a = dists.log_prob(actions).reshape(batch_size, self.n_agents)
                        actions = actions.reshape(batch_size, self.n_agents)
                    else:
                        hidden_state, dists = self.policy(obs_in, agents_id, avail_actions=avail_actions)
                        actions = dists.stochastic_sample()
                        log_pi_a = dists.log_prob(actions)
                    return hidden_state, actions.detach().cpu().numpy(), log_pi_a.detach().cpu().numpy()

                def values(self, obs_n, *rnn_hidden, state=None):
                    batch_size = len(obs_n)
                    agents_id = torch.eye(self.n_agents).unsqueeze(0).expand(batch_size, -1, -1).to(self.device)
                    # build critic input
                    if self.use_global_state:
                        state = torch.Tensor(state).unsqueeze(1).to(self.device)
                        critic_in = state.expand(-1, self.n_agents, -1)
                    else:
                        critic_in = torch.Tensor(obs_n).to(self.device)
                    # get critic values
                    if self.use_recurrent:
                        hidden_state, values_n = self.policy.get_values(critic_in.unsqueeze(2),  # add a sequence length axis.
                                                                        agents_id.unsqueeze(2),
                                                                        *rnn_hidden)
                        values_n = values_n.squeeze(2)
                    else:
                        hidden_state, values_n = self.policy.get_values(critic_in, agents_id)

                    return hidden_state, values_n.detach().cpu().numpy()

                def train(self, i_step, **kwargs):
                    if self.memory.full:
                        info_train = {}
                        indexes = np.arange(self.buffer_size)
                        for _ in range(self.n_epoch):
                            np.random.shuffle(indexes)
                            for start in range(0, self.buffer_size, self.batch_size):
                                end = start + self.batch_size
                                sample_idx = indexes[start:end]
                                sample = self.memory.sample(sample_idx)
                                if self.use_recurrent:
                                    info_train = self.learner.update_recurrent(sample)
                                else:
                                    info_train = self.learner.update(sample)
                        self.learner.lr_decay(i_step)
                        self.memory.clear()
                        return info_train
                    else:
                        return {}





    .. group-tab:: TensorFlow
    
        .. code-block:: python

            from xuance.tensorflow.agents import *


            class IPPO_Agents(MARLAgents):
                def __init__(self,
                             config: Namespace,
                             envs: DummyVecEnv_Pettingzoo,
                             device: str = "cpu:0"):
                    self.gamma = config.gamma
                    self.n_envs = envs.num_envs
                    self.n_size = config.n_size
                    self.n_epoch = config.n_epoch
                    self.n_minibatch = config.n_minibatch
                    if config.state_space is not None:
                        config.dim_state, state_shape = config.state_space.shape[0], config.state_space.shape
                    else:
                        config.dim_state, state_shape = None, None

                    input_representation = get_repre_in(config)
                    self.use_recurrent = config.use_recurrent
                    self.use_global_state = config.use_global_state
                    # create representation for actor
                    kwargs_rnn = {"N_recurrent_layers": config.N_recurrent_layers,
                                  "dropout": config.dropout,
                                  "rnn": config.rnn} if self.use_recurrent else {}
                    representation = REGISTRY_Representation[config.representation](*input_representation, **kwargs_rnn)
                    # create representation for critic
                    input_representation[0] = (config.dim_state,) if self.use_global_state else (config.dim_obs,)
                    representation_critic = REGISTRY_Representation[config.representation](*input_representation, **kwargs_rnn)
                    # create policy
                    input_policy = get_policy_in_marl(config, (representation, representation_critic))
                    policy = REGISTRY_Policy[config.policy](*input_policy,
                                                            use_recurrent=config.use_recurrent,
                                                            rnn=config.rnn,
                                                            gain=config.gain)
                    lr_scheduler = MyLinearLR(config.learning_rate, start_factor=1.0, end_factor=0.5,
                                              total_iters=get_total_iters(config.agent_name, config))
                    optimizer = tk.optimizers.Adam(lr_scheduler)
                    self.observation_space = envs.observation_space
                    self.action_space = envs.action_space
                    self.auxiliary_info_shape = {}

                    buffer = MARL_OnPolicyBuffer_RNN if self.use_recurrent else MARL_OnPolicyBuffer
                    input_buffer = (config.n_agents, config.state_space.shape, config.obs_shape, config.act_shape, config.rew_shape,
                                    config.done_shape, envs.num_envs, config.n_size,
                                    config.use_gae, config.use_advnorm, config.gamma, config.gae_lambda)
                    memory = buffer(*input_buffer, max_episode_length=envs.max_episode_length, dim_act=config.dim_act)
                    self.buffer_size = memory.buffer_size
                    self.batch_size = self.buffer_size // self.n_minibatch

                    learner = IPPO_Learner(config, policy, optimizer, config.device, config.model_dir, config.gamma)
                    super(IPPO_Agents, self).__init__(config, envs, policy, memory, learner, device,
                                                      config.log_dir, config.model_dir)
                    self.share_values = True if config.rew_shape[0] == 1 else False
                    self.on_policy = True

                def act(self, obs_n, *rnn_hidden, avail_actions=None, state=None, test_mode=False):
                    batch_size = len(obs_n)
                    with tf.device(self.device):
                        agents_id = tf.tile(tf.expand_dims(tf.eye(self.n_agents), axis=0), multiples=(batch_size, 1, 1))
                        inputs_policy = {"obs": tf.convert_to_tensor(obs_n), "ids": agents_id}
                        _, dists = self.policy(inputs_policy)
                        acts = dists.stochastic_sample()
                        log_pi_a = dists.log_prob(acts)
                    return rnn_hidden, acts.numpy(), log_pi_a.numpy()

                def values(self, obs_n, *rnn_hidden, state=None):
                    batch_size = len(state)
                    agents_id = tf.tile(tf.expand_dims(tf.eye(self.n_agents), axis=0), multiples=(batch_size, 1, 1))
                    rnn_hidden, values_n = self.policy.get_values(obs_n, agents_id, rnn_hidden)
                    values_n = tf.expand_dims(values_n, -1)
                    return rnn_hidden, values_n.numpy()

                def train(self, i_step, **kwargs):
                    if self.memory.full:
                        info_train = {}
                        indexes = np.arange(self.buffer_size)
                        for _ in range(self.n_epoch):
                            np.random.shuffle(indexes)
                            for start in range(0, self.buffer_size, self.batch_size):
                                end = start + self.batch_size
                                sample_idx = indexes[start:end]
                                sample = self.memory.sample(sample_idx)
                                if self.use_recurrent:
                                    info_train = self.learner.update_recurrent(sample)
                                else:
                                    info_train = self.learner.update(sample)
                        self.learner.lr_decay(i_step)
                        self.memory.clear()
                        return info_train
                    else:
                        return {}


    .. group-tab:: MindSpore

        .. code-block:: python

            from xuance.mindspore.agents import *


            class IPPO_Agents(MARLAgents):
                def __init__(self,
                             config: Namespace,
                             envs: DummyVecEnv_Pettingzoo):
                    self.gamma = config.gamma
                    self.n_envs = envs.num_envs
                    self.n_size = config.n_size
                    self.n_epoch = config.n_epoch
                    self.n_minibatch = config.n_minibatch
                    if config.state_space is not None:
                        config.dim_state, state_shape = config.state_space.shape[0], config.state_space.shape
                    else:
                        config.dim_state, state_shape = None, None

                    input_representation = get_repre_in(config)
                    self.use_recurrent = config.use_recurrent
                    self.use_global_state = config.use_global_state
                    # create representation for actor
                    kwargs_rnn = {"N_recurrent_layers": config.N_recurrent_layers,
                                  "dropout": config.dropout,
                                  "rnn": config.rnn} if self.use_recurrent else {}
                    representation = REGISTRY_Representation[config.representation](*input_representation, **kwargs_rnn)
                    # create representation for critic
                    input_representation[0] = (config.dim_state,) if self.use_global_state else (config.dim_obs,)
                    representation_critic = REGISTRY_Representation[config.representation](*input_representation, **kwargs_rnn)
                    # create policy
                    input_policy = get_policy_in_marl(config, (representation, representation_critic))
                    policy = REGISTRY_Policy[config.policy](*input_policy,
                                                            use_recurrent=config.use_recurrent,
                                                            rnn=config.rnn,
                                                            gain=config.gain)
                    scheduler = lr_decay_model(learning_rate=config.learning_rate, decay_rate=0.5,
                                               decay_steps=get_total_iters(config.agent_name, config))
                    optimizer = Adam(policy.trainable_params(), config.learning_rate, eps=1e-5)
                    self.observation_space = envs.observation_space
                    self.action_space = envs.action_space
                    self.auxiliary_info_shape = {}

                    buffer = MARL_OnPolicyBuffer_RNN if self.use_recurrent else MARL_OnPolicyBuffer
                    input_buffer = (config.n_agents, config.state_space.shape, config.obs_shape, config.act_shape, config.rew_shape,
                                    config.done_shape, envs.num_envs, config.n_size,
                                    config.use_gae, config.use_advnorm, config.gamma, config.gae_lambda)
                    memory = buffer(*input_buffer, max_episode_length=envs.max_episode_length, dim_act=config.dim_act)
                    self.buffer_size = memory.buffer_size
                    self.batch_size = self.buffer_size // self.n_minibatch

                    learner = IPPO_Learner(config, policy, optimizer, scheduler, config.model_dir, config.gamma)
                    super(IPPO_Agents, self).__init__(config, envs, policy, memory, learner, config.log_dir, config.model_dir)
                    self.share_values = True if config.rew_shape[0] == 1 else False
                    self.on_policy = True

                def act(self, obs_n, *rnn_hidden, avail_actions=None, state=None, test_mode=False):
                    batch_size = len(obs_n)
                    agents_id = ops.broadcast_to(self.expand_dims(self.eye(self.n_agents, self.n_agents, ms.float32), 0),
                                                 (batch_size, -1, -1))
                    obs_in = Tensor(obs_n).view(batch_size, self.n_agents, -1)
                    if self.use_recurrent:
                        batch_agents = batch_size * self.n_agents
                        hidden_state, act_probs = self.policy(obs_in.view(batch_agents, 1, -1),
                                                              agents_id.view(batch_agents, 1, -1),
                                                              *rnn_hidden,
                                                              avail_actions=avail_actions.reshape(batch_agents, 1, -1))
                        actions = self.policy.actor.sample(act_probs)
                        log_pi_a = self.policy.actor.log_prob(value=actions, probs=act_probs)
                        actions = actions.reshape(batch_size, self.n_agents)
                    else:
                        hidden_state, act_probs = self.policy(obs_in, agents_id, avail_actions=avail_actions)
                        actions = self.policy.actor.sample(act_probs)
                        log_pi_a = self.policy.actor.log_prob(value=actions, probs=act_probs)
                    return hidden_state, actions.asnumpy(), log_pi_a.asnumpy()

                def values(self, obs_n, *rnn_hidden, state=None):
                    batch_size = len(obs_n)
                    agents_id = ops.broadcast_to(self.expand_dims(self.eye(self.n_agents, self.n_agents, ms.float32), 0),
                                                 (batch_size, -1, -1))
                    # build critic input
                    if self.use_global_state:
                        state = Tensor(state).unsqueeze(1)
                        critic_in = state.expand(-1, self.n_agents, -1)
                    else:
                        critic_in = Tensor(obs_n)
                    # get critic values
                    if self.use_recurrent:
                        hidden_state, values_n = self.policy.get_values(critic_in.unsqueeze(2),  # add a sequence length axis.
                                                                        agents_id.unsqueeze(2),
                                                                        *rnn_hidden)
                        values_n = values_n.squeeze(2)
                    else:
                        hidden_state, values_n = self.policy.get_values(critic_in, agents_id)

                    return hidden_state, values_n.asnumpy()

                def train(self, i_step, **kwargs):
                    if self.memory.full:
                        info_train = {}
                        indexes = np.arange(self.buffer_size)
                        for _ in range(self.n_epoch):
                            np.random.shuffle(indexes)
                            for start in range(0, self.buffer_size, self.batch_size):
                                end = start + self.batch_size
                                sample_idx = indexes[start:end]
                                sample = self.memory.sample(sample_idx)
                                if self.use_recurrent:
                                    info_train = self.learner.update_recurrent(sample)
                                else:
                                    info_train = self.learner.update(sample)
                        self.learner.lr_decay(i_step)
                        self.memory.clear()
                        return info_train
                    else:
                        return {}
