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Source code for ding.policy.qmix

from typing import List, Dict, Any, Tuple, Optional
from collections import namedtuple
import copy
import torch

from ding.torch_utils import RMSprop, to_device
from ding.rl_utils import v_1step_td_data, v_1step_td_error, get_train_sample
from ding.model import model_wrap
from ding.utils import POLICY_REGISTRY
from ding.utils.data import timestep_collate, default_collate, default_decollate
from .base_policy import Policy


[docs]@POLICY_REGISTRY.register('qmix') class QMIXPolicy(Policy): """ Overview: Policy class of QMIX algorithm. QMIX is a multi-agent reinforcement learning algorithm, \ you can view the paper in the following link https://arxiv.org/abs/1803.11485. Config: == ==================== ======== ============== ======================================== ======================= ID Symbol Type Default Value Description Other(Shape) == ==================== ======== ============== ======================================== ======================= 1 ``type`` str qmix | RL policy register name, refer to | this arg is optional, | registry ``POLICY_REGISTRY`` | a placeholder 2 ``cuda`` bool True | Whether to use cuda for network | this arg can be diff- | erent from modes 3 ``on_policy`` bool False | Whether the RL algorithm is on-policy | or off-policy 4. ``priority`` bool False | Whether use priority(PER) | priority sample, | update priority 5 | ``priority_`` bool False | Whether use Importance Sampling | IS weight | ``IS_weight`` | Weight to correct biased update. 6 | ``learn.update_`` int 20 | How many updates(iterations) to train | this args can be vary | ``per_collect`` | after collector's one collection. Only | from envs. Bigger val | valid in serial training | means more off-policy 7 | ``learn.target_`` float 0.001 | Target network update momentum | between[0,1] | ``update_theta`` | parameter. 8 | ``learn.discount`` float 0.99 | Reward's future discount factor, aka. | may be 1 when sparse | ``_factor`` | gamma | reward env == ==================== ======== ============== ======================================== ======================= """ config = dict( # (str) RL policy register name (refer to function "POLICY_REGISTRY"). type='qmix', # (bool) Whether to use cuda for network. cuda=True, # (bool) Whether the RL algorithm is on-policy or off-policy. on_policy=False, # (bool) Whether use priority(priority sample, IS weight, update priority) priority=False, # (bool) Whether use Importance Sampling Weight to correct biased update. If True, priority must be True. priority_IS_weight=False, # learn_mode config learn=dict( # (int) How many updates(iterations) to train after collector's one collection. # Bigger "update_per_collect" means bigger off-policy. # collect data -> update policy-> collect data -> ... update_per_collect=20, # (int) How many samples in a training batch. batch_size=32, # (float) The step size of gradient descent. learning_rate=0.0005, clip_value=100, # (float) Target network update momentum parameter, in [0, 1]. target_update_theta=0.008, # (float) The discount factor for future rewards, in [0, 1]. discount_factor=0.99, # (bool) Whether to use double DQN mechanism(target q for surpassing over estimation). double_q=False, ), # collect_mode config collect=dict( # (int) How many training samples collected in one collection procedure. # In each collect phase, we collect a total of <n_sample> sequence samples, a sample with length unroll_len. # n_sample=32, # (int) Split trajectories into pieces with length ``unroll_len``, the length of timesteps # in each forward when training. In qmix, it is greater than 1 because there is RNN. unroll_len=10, ), eval=dict(), # for compatibility other=dict( eps=dict( # (str) Type of epsilon decay. type='exp', # (float) Start value for epsilon decay, in [0, 1]. start=1, # (float) Start value for epsilon decay, in [0, 1]. end=0.05, # (int) Decay length(env step). decay=50000, ), replay_buffer=dict( # (int) Maximum size of replay buffer. Usually, larger buffer size is better. replay_buffer_size=5000, ), ), )
[docs] def default_model(self) -> Tuple[str, List[str]]: """ Overview: Return this algorithm default model setting for demonstration. Returns: - model_info (:obj:`Tuple[str, List[str]]`): model name and mode import_names .. note:: The user can define and use customized network model but must obey the same inferface definition indicated \ by import_names path. For QMIX, ``ding.model.qmix.qmix`` """ return 'qmix', ['ding.model.template.qmix']
[docs] def _init_learn(self) -> None: """ Overview: Initialize the learn mode of policy, including some attributes and modules. For QMIX, it mainly contains \ optimizer, algorithm-specific arguments such as gamma, main and target model. Because of the use of RNN, \ all the models should be wrappered with ``hidden_state`` which needs to be initialized with proper size. This method will be called in ``__init__`` method if ``learn`` field is in ``enable_field``. .. tip:: For multi-agent algorithm, we often need to use ``agent_num`` to initialize some necessary variables. .. note:: For the member variables that need to be saved and loaded, please refer to the ``_state_dict_learn`` \ and ``_load_state_dict_learn`` methods. .. note:: For the member variables that need to be monitored, please refer to the ``_monitor_vars_learn`` method. .. note:: If you want to set some spacial member variables in ``_init_learn`` method, you'd better name them \ with prefix ``_learn_`` to avoid conflict with other modes, such as ``self._learn_attr1``. - agent_num (:obj:`int`): Since this is a multi-agent algorithm, we need to input the agent num. """ self._priority = self._cfg.priority self._priority_IS_weight = self._cfg.priority_IS_weight assert not self._priority and not self._priority_IS_weight, "Priority is not implemented in QMIX" self._optimizer = RMSprop( params=self._model.parameters(), lr=self._cfg.learn.learning_rate, alpha=0.99, eps=0.00001, weight_decay=1e-5 ) self._gamma = self._cfg.learn.discount_factor self._target_model = copy.deepcopy(self._model) self._target_model = model_wrap( self._target_model, wrapper_name='target', update_type='momentum', update_kwargs={'theta': self._cfg.learn.target_update_theta} ) self._target_model = model_wrap( self._target_model, wrapper_name='hidden_state', state_num=self._cfg.learn.batch_size, init_fn=lambda: [None for _ in range(self._cfg.model.agent_num)] ) self._learn_model = model_wrap( self._model, wrapper_name='hidden_state', state_num=self._cfg.learn.batch_size, init_fn=lambda: [None for _ in range(self._cfg.model.agent_num)] ) self._learn_model.reset() self._target_model.reset()
def _data_preprocess_learn(self, data: List[Dict[str, Any]]) -> Dict[str, Any]: """ Overview: Preprocess the data to fit the required data format for learning Arguments: - data (:obj:`List[Dict[str, Any]]`): the data collected from collect function Returns: - data (:obj:`Dict[str, Any]`): the processed data, from \ [len=B, ele={dict_key: [len=T, ele=Tensor(any_dims)]}] -> {dict_key: Tensor([T, B, any_dims])} """ # data preprocess data = timestep_collate(data) if self._cuda: data = to_device(data, self._device) data['weight'] = data.get('weight', None) data['done'] = data['done'].float() return data
[docs] def _forward_learn(self, data: List[List[Dict[str, Any]]]) -> Dict[str, Any]: """ Overview: Policy forward function of learn mode (training policy and updating parameters). Forward means \ that the policy inputs some training batch data (trajectory for QMIX) from the replay buffer and then \ returns the output result, including various training information such as loss, q value, grad_norm. Arguments: - data (:obj:`List[List[Dict[int, Any]]]`): The input data used for policy forward, including a batch of \ training samples. For each dict element, the key of the dict is the name of data items and the \ value is the corresponding data. Usually, the value is torch.Tensor or np.ndarray or there dict/list \ combinations. In the ``_forward_learn`` method, data often need to first be stacked in the time and \ batch dimension by the utility functions ``self._data_preprocess_learn``. \ For QMIX, each element in list is a trajectory with the length of ``unroll_len``, and the element in \ trajectory list is a dict containing at least the following keys: ``obs``, ``action``, ``prev_state``, \ ``reward``, ``next_obs``, ``done``. Sometimes, it also contains other keys such as ``weight`` \ and ``value_gamma``. Returns: - info_dict (:obj:`Dict[str, Any]`): The information dict that indicated training result, which will be \ recorded in text log and tensorboard, values must be python scalar or a list of scalars. For the \ detailed definition of the dict, refer to the code of ``_monitor_vars_learn`` method. .. note:: The input value can be torch.Tensor or dict/list combinations and current policy supports all of them. \ For the data type that not supported, the main reason is that the corresponding model does not support it. \ You can implement you own model rather than use the default model. For more information, please raise an \ issue in GitHub repo and we will continue to follow up. .. note:: For more detailed examples, please refer to our unittest for QMIXPolicy: ``ding.policy.tests.test_qmix``. """ data = self._data_preprocess_learn(data) # ==================== # Q-mix forward # ==================== self._learn_model.train() self._target_model.train() # for hidden_state plugin, we need to reset the main model and target model self._learn_model.reset(state=data['prev_state'][0]) self._target_model.reset(state=data['prev_state'][0]) inputs = {'obs': data['obs'], 'action': data['action']} total_q = self._learn_model.forward(inputs, single_step=False)['total_q'] if self._cfg.learn.double_q: next_inputs = {'obs': data['next_obs']} self._learn_model.reset(state=data['prev_state'][1]) logit_detach = self._learn_model.forward(next_inputs, single_step=False)['logit'].clone().detach() next_inputs = {'obs': data['next_obs'], 'action': logit_detach.argmax(dim=-1)} else: next_inputs = {'obs': data['next_obs']} with torch.no_grad(): target_total_q = self._target_model.forward(next_inputs, single_step=False)['total_q'] with torch.no_grad(): if data['done'] is not None: target_v = self._gamma * (1 - data['done']) * target_total_q + data['reward'] else: target_v = self._gamma * target_total_q + data['reward'] data = v_1step_td_data(total_q, target_total_q, data['reward'], data['done'], data['weight']) loss, td_error_per_sample = v_1step_td_error(data, self._gamma) # ==================== # Q-mix update # ==================== self._optimizer.zero_grad() loss.backward() grad_norm = torch.nn.utils.clip_grad_norm_(self._model.parameters(), self._cfg.learn.clip_value) self._optimizer.step() # ============= # after update # ============= self._target_model.update(self._learn_model.state_dict()) return { 'cur_lr': self._optimizer.defaults['lr'], 'total_loss': loss.item(), 'total_q': total_q.mean().item() / self._cfg.model.agent_num, 'target_reward_total_q': target_v.mean().item() / self._cfg.model.agent_num, 'target_total_q': target_total_q.mean().item() / self._cfg.model.agent_num, 'grad_norm': grad_norm, }
[docs] def _reset_learn(self, data_id: Optional[List[int]] = None) -> None: """ Overview: Reset some stateful variables for learn mode when necessary, such as the hidden state of RNN or the \ memory bank of some special algortihms. If ``data_id`` is None, it means to reset all the stateful \ varaibles. Otherwise, it will reset the stateful variables according to the ``data_id``. For example, \ different trajectories in ``data_id`` will have different hidden state in RNN. Arguments: - data_id (:obj:`Optional[List[int]]`): The id of the data, which is used to reset the stateful variables \ (i.e. RNN hidden_state in QMIX) specified by ``data_id``. """ self._learn_model.reset(data_id=data_id)
[docs] def _state_dict_learn(self) -> Dict[str, Any]: """ Overview: Return the state_dict of learn mode, usually including model, target_model and optimizer. Returns: - state_dict (:obj:`Dict[str, Any]`): The dict of current policy learn state, for saving and restoring. """ return { 'model': self._learn_model.state_dict(), 'target_model': self._target_model.state_dict(), 'optimizer': self._optimizer.state_dict(), }
[docs] def _load_state_dict_learn(self, state_dict: Dict[str, Any]) -> None: """ Overview: Load the state_dict variable into policy learn mode. Arguments: - state_dict (:obj:`Dict[str, Any]`): The dict of policy learn state saved before. .. tip:: If you want to only load some parts of model, you can simply set the ``strict`` argument in \ load_state_dict to ``False``, or refer to ``ding.torch_utils.checkpoint_helper`` for more \ complicated operation. """ self._learn_model.load_state_dict(state_dict['model']) self._target_model.load_state_dict(state_dict['target_model']) self._optimizer.load_state_dict(state_dict['optimizer'])
[docs] def _init_collect(self) -> None: """ Overview: Initialize the collect mode of policy, including related attributes and modules. For QMIX, it contains the \ collect_model to balance the exploration and exploitation with epsilon-greedy sample mechanism and \ maintain the hidden state of rnn. Besides, there are some initialization operations about other \ algorithm-specific arguments such as burnin_step, unroll_len and nstep. This method will be called in ``__init__`` method if ``collect`` field is in ``enable_field``. .. note:: If you want to set some spacial member variables in ``_init_collect`` method, you'd better name them \ with prefix ``_collect_`` to avoid conflict with other modes, such as ``self._collect_attr1``. """ self._unroll_len = self._cfg.collect.unroll_len self._collect_model = model_wrap( self._model, wrapper_name='hidden_state', state_num=self._cfg.collect.env_num, save_prev_state=True, init_fn=lambda: [None for _ in range(self._cfg.model.agent_num)] ) self._collect_model = model_wrap(self._collect_model, wrapper_name='eps_greedy_sample') self._collect_model.reset()
[docs] def _forward_collect(self, data: Dict[int, Any], eps: float) -> Dict[int, Any]: """ Overview: Policy forward function of collect mode (collecting training data by interacting with envs). Forward means \ that the policy gets some necessary data (mainly observation) from the envs and then returns the output \ data, such as the action to interact with the envs. Besides, this policy also needs ``eps`` argument for \ exploration, i.e., classic epsilon-greedy exploration strategy. Arguments: - data (:obj:`Dict[int, Any]`): The input data used for policy forward, including at least the obs. The \ key of the dict is environment id and the value is the corresponding data of the env. - eps (:obj:`float`): The epsilon value for exploration. Returns: - output (:obj:`Dict[int, Any]`): The output data of policy forward, including at least the action and \ other necessary data (prev_state) for learn mode defined in ``self._process_transition`` method. The \ key of the dict is the same as the input data, i.e. environment id. .. note:: RNN's hidden states are maintained in the policy, so we don't need pass them into data but to reset the \ hidden states with ``_reset_collect`` method when episode ends. Besides, the previous hidden states are \ necessary for training, so we need to return them in ``_process_transition`` method. .. note:: The input value can be torch.Tensor or dict/list combinations and current policy supports all of them. \ For the data type that not supported, the main reason is that the corresponding model does not support it. \ You can implement you own model rather than use the default model. For more information, please raise an \ issue in GitHub repo and we will continue to follow up. .. note:: For more detailed examples, please refer to our unittest for QMIXPolicy: ``ding.policy.tests.test_qmix``. """ data_id = list(data.keys()) data = default_collate(list(data.values())) if self._cuda: data = to_device(data, self._device) data = {'obs': data} self._collect_model.eval() with torch.no_grad(): output = self._collect_model.forward(data, eps=eps, data_id=data_id) if self._cuda: output = to_device(output, 'cpu') output = default_decollate(output) return {i: d for i, d in zip(data_id, output)}
[docs] def _reset_collect(self, data_id: Optional[List[int]] = None) -> None: """ Overview: Reset some stateful variables for eval mode when necessary, such as the hidden state of RNN or the \ memory bank of some special algortihms. If ``data_id`` is None, it means to reset all the stateful \ varaibles. Otherwise, it will reset the stateful variables according to the ``data_id``. For example, \ different environments/episodes in evaluation in ``data_id`` will have different hidden state in RNN. Arguments: - data_id (:obj:`Optional[List[int]]`): The id of the data, which is used to reset the stateful variables \ (i.e., RNN hidden_state in QMIX) specified by ``data_id``. """ self._collect_model.reset(data_id=data_id)
[docs] def _process_transition(self, obs: torch.Tensor, policy_output: Dict[str, torch.Tensor], timestep: namedtuple) -> Dict[str, torch.Tensor]: """ Overview: Process and pack one timestep transition data into a dict, which can be directly used for training and \ saved in replay buffer. For QMIX, it contains obs, next_obs, action, prev_state, reward, done. Arguments: - obs (:obj:`torch.Tensor`): The env observation of current timestep, usually including ``agent_obs`` \ and ``global_obs`` in multi-agent environment like MPE and SMAC. - policy_output (:obj:`Dict[str, torch.Tensor]`): The output of the policy network with the observation \ as input. For QMIX, it contains the action and the prev_state of RNN. - timestep (:obj:`namedtuple`): The execution result namedtuple returned by the environment step method, \ except all the elements have been transformed into tensor data. Usually, it contains the next obs, \ reward, done, info, etc. Returns: - transition (:obj:`Dict[str, torch.Tensor]`): The processed transition data of the current timestep. """ transition = { 'obs': obs, 'next_obs': timestep.obs, 'prev_state': policy_output['prev_state'], 'action': policy_output['action'], 'reward': timestep.reward, 'done': timestep.done, } return transition
[docs] def _get_train_sample(self, transitions: List[Dict[str, Any]]) -> List[Dict[str, Any]]: """ Overview: For a given trajectory (transitions, a list of transition) data, process it into a list of sample that \ can be used for training directly. In QMIX, a train sample is processed transitions with unroll_len \ length. This method is usually used in collectors to execute necessary \ RL data preprocessing before training, which can help learner amortize revelant time consumption. \ In addition, you can also implement this method as an identity function and do the data processing \ in ``self._forward_learn`` method. Arguments: - transitions (:obj:`List[Dict[str, Any]`): The trajectory data (a list of transition), each element is \ the same format as the return value of ``self._process_transition`` method. Returns: - samples (:obj:`List[Dict[str, Any]]`): The processed train samples, each sample is a fixed-length \ trajectory, and each element in a sample is the similar format as input transitions. """ return get_train_sample(transitions, self._unroll_len)
def _init_eval(self) -> None: """ Overview: Initialize the eval mode of policy, including related attributes and modules. For QMIX, it contains the \ eval model to greedily select action with argmax q_value mechanism and main the hidden state. This method will be called in ``__init__`` method if ``eval`` field is in ``enable_field``. .. note:: If you want to set some spacial member variables in ``_init_eval`` method, you'd better name them \ with prefix ``_eval_`` to avoid conflict with other modes, such as ``self._eval_attr1``. """ self._eval_model = model_wrap( self._model, wrapper_name='hidden_state', state_num=self._cfg.eval.env_num, save_prev_state=True, init_fn=lambda: [None for _ in range(self._cfg.model.agent_num)] ) self._eval_model = model_wrap(self._eval_model, wrapper_name='argmax_sample') self._eval_model.reset() def _forward_eval(self, data: dict) -> dict: """ Overview: Policy forward function of eval mode (evaluation policy performance by interacting with envs). Forward \ means that the policy gets some necessary data (mainly observation) from the envs and then returns the \ action to interact with the envs. ``_forward_eval`` often use argmax sample method to get actions that \ q_value is the highest. Arguments: - data (:obj:`Dict[int, Any]`): The input data used for policy forward, including at least the obs. The \ key of the dict is environment id and the value is the corresponding data of the env. Returns: - output (:obj:`Dict[int, Any]`): The output data of policy forward, including at least the action. The \ key of the dict is the same as the input data, i.e. environment id. .. note:: RNN's hidden states are maintained in the policy, so we don't need pass them into data but to reset the \ hidden states with ``_reset_eval`` method when the episode ends. .. note:: The input value can be torch.Tensor or dict/list combinations and current policy supports all of them. \ For the data type that not supported, the main reason is that the corresponding model does not support it. \ You can implement you own model rather than use the default model. For more information, please raise an \ issue in GitHub repo and we will continue to follow up. .. note:: For more detailed examples, please refer to our unittest for QMIXPolicy: ``ding.policy.tests.test_qmix``. """ data_id = list(data.keys()) data = default_collate(list(data.values())) if self._cuda: data = to_device(data, self._device) data = {'obs': data} self._eval_model.eval() with torch.no_grad(): output = self._eval_model.forward(data, data_id=data_id) if self._cuda: output = to_device(output, 'cpu') output = default_decollate(output) return {i: d for i, d in zip(data_id, output)}
[docs] def _reset_eval(self, data_id: Optional[List[int]] = None) -> None: """ Overview: Reset some stateful variables for eval mode when necessary, such as the hidden state of RNN or the \ memory bank of some special algortihms. If ``data_id`` is None, it means to reset all the stateful \ varaibles. Otherwise, it will reset the stateful variables according to the ``data_id``. For example, \ different environments/episodes in evaluation in ``data_id`` will have different hidden state in RNN. Arguments: - data_id (:obj:`Optional[List[int]]`): The id of the data, which is used to reset the stateful variables \ (i.e., RNN hidden_state in QMIX) specified by ``data_id``. """ self._eval_model.reset(data_id=data_id)
[docs] def _monitor_vars_learn(self) -> List[str]: """ Overview: Return the necessary keys for logging the return dict of ``self._forward_learn``. The logger module, such \ as text logger, tensorboard logger, will use these keys to save the corresponding data. Returns: - necessary_keys (:obj:`List[str]`): The list of the necessary keys to be logged. """ return ['cur_lr', 'total_loss', 'total_q', 'target_total_q', 'grad_norm', 'target_reward_total_q']