from typing import TYPE_CHECKING, List, Any, Union from easydict import EasyDict import numpy as np import torch import copy from lzero.policy import InverseScalarTransform, to_detach_cpu_numpy from lzero.mcts.ptree import MinMaxStatsList if TYPE_CHECKING: import lzero.mcts.ptree.ptree_sez as ptree # ============================================================== # Sampled EfficientZero # ============================================================== import lzero.mcts.ptree.ptree_sez as tree_sez class SampledEfficientZeroMCTSPtree(object): """ Overview: MCTSPtree for Sampled EfficientZero. The core ``batch_traverse`` and ``batch_backpropagate`` function is implemented in python. Interfaces: __init__, roots, search """ # the default_config for SampledEfficientZeroMCTSPtree. config = dict( # (float) The alpha value used in the Dirichlet distribution for exploration at the root node of the search tree. root_dirichlet_alpha=0.3, # (float) The noise weight at the root node of the search tree. root_noise_weight=0.25, # (int) The base constant used in the PUCT formula for balancing exploration and exploitation during tree search. pb_c_base=19652, # (float) The initialization constant used in the PUCT formula for balancing exploration and exploitation during tree search. pb_c_init=1.25, # (float) The maximum change in value allowed during the backup step of the search tree update. value_delta_max=0.01, ) @classmethod def default_config(cls: type) -> EasyDict: cfg = EasyDict(copy.deepcopy(cls.config)) cfg.cfg_type = cls.__name__ + 'Dict' return cfg def __init__(self, cfg: EasyDict = None) -> None: """ Overview: Use the default configuration mechanism. If a user passes in a cfg with a key that matches an existing key in the default configuration, the user-provided value will override the default configuration. Otherwise, the default configuration will be used. """ default_config = self.default_config() default_config.update(cfg) self._cfg = default_config self.inverse_scalar_transform_handle = InverseScalarTransform( self._cfg.model.support_scale, self._cfg.device, self._cfg.model.categorical_distribution ) @classmethod def roots( cls: int, root_num: int, legal_action_lis: List[Any], action_space_size: int, num_of_sampled_actions: int, continuous_action_space: bool ) -> "ptree.Roots": """ Overview: Initialization of CNode with root_num, legal_actions_list, action_space_size, num_of_sampled_actions, continuous_action_space. Arguments: - root_num (:obj:'int'): the number of the current root. - legal_action_lis (:obj:'List'): the vector of the legal action of this root. - action_space_size (:obj:'int'): the size of action space of the current env. - num_of_sampled_actions (:obj:'int'): the number of sampled actions, i.e. K in the Sampled MuZero papers. - continuous_action_space (:obj:'bool'): whether the action space is continous in current env. """ import lzero.mcts.ptree.ptree_sez as ptree return ptree.Roots( root_num, legal_action_lis, action_space_size, num_of_sampled_actions, continuous_action_space ) def search( self, roots: Any, model: torch.nn.Module, latent_state_roots: List[Any], reward_hidden_state_roots: List[Any], to_play: Union[int, List[Any]] = -1 ) -> None: """ Overview: Do MCTS for the roots (a batch of root nodes in parallel). Parallel in model inference. Use the python ctree. Arguments: - roots (:obj:`Any`): a batch of expanded root nodes - latent_state_roots (:obj:`list`): the hidden states of the roots - reward_hidden_state_roots (:obj:`list`): the value prefix hidden states in LSTM of the roots - to_play (:obj:`list`): the to_play list used in in self-play-mode board games """ with torch.no_grad(): model.eval() # preparation some constant batch_size = roots.num pb_c_base, pb_c_init, discount_factor = self._cfg.pb_c_base, self._cfg.pb_c_init, self._cfg.discount_factor # the data storage of latent states: storing the latent state of all the nodes in one search. latent_state_batch_in_search_path = [latent_state_roots] # the data storage of value prefix hidden states in LSTM reward_hidden_state_c_batch = [reward_hidden_state_roots[0]] reward_hidden_state_h_batch = [reward_hidden_state_roots[1]] # minimax value storage min_max_stats_lst = MinMaxStatsList(batch_size) for simulation_index in range(self._cfg.num_simulations): # In each simulation, we expanded a new node, so in one search, we have ``num_simulations`` num of nodes at most. latent_states = [] hidden_states_c_reward = [] hidden_states_h_reward = [] # prepare a result wrapper to transport results between python and c++ parts results = tree_sez.SearchResults(num=batch_size) # latent_state_index_in_search_path: the first index of leaf node states in latent_state_batch_in_search_path, i.e. is current_latent_state_index in one the search. # latent_state_index_in_batch: the second index of leaf node states in latent_state_batch_in_search_path, i.e. the index in the batch, whose maximum is ``batch_size``. # e.g. the latent state of the leaf node in (x, y) is latent_state_batch_in_search_path[x, y], where x is current_latent_state_index, y is batch_index. # The index of value prefix hidden state of the leaf node are in the same manner. """ MCTS stage 1: Selection Each simulation starts from the internal root state s0, and finishes when the simulation reaches a leaf node s_l. """ latent_state_index_in_search_path, latent_state_index_in_batch, last_actions, virtual_to_play = tree_sez.batch_traverse( roots, pb_c_base, pb_c_init, discount_factor, min_max_stats_lst, results, copy.deepcopy(to_play), self._cfg.model.continuous_action_space ) # obtain the search horizon for leaf nodes search_lens = results.search_lens # obtain the latent state for leaf node for ix, iy in zip(latent_state_index_in_search_path, latent_state_index_in_batch): latent_states.append(latent_state_batch_in_search_path[ix][iy]) hidden_states_c_reward.append(reward_hidden_state_c_batch[ix][0][iy]) hidden_states_h_reward.append(reward_hidden_state_h_batch[ix][0][iy]) latent_states = torch.from_numpy(np.asarray(latent_states)).to(self._cfg.device).float() hidden_states_c_reward = torch.from_numpy(np.asarray(hidden_states_c_reward)).to(self._cfg.device ).unsqueeze(0) hidden_states_h_reward = torch.from_numpy(np.asarray(hidden_states_h_reward)).to(self._cfg.device ).unsqueeze(0) if self._cfg.model.continuous_action_space is True: # continuous action last_actions = torch.from_numpy(np.asarray(last_actions)).to(self._cfg.device).float() else: # discrete action last_actions = torch.from_numpy(np.asarray(last_actions)).to(self._cfg.device).long() """ MCTS stage 2: Expansion At the final time-step l of the simulation, the next_latent_state and reward/value_prefix are computed by the dynamics function. Then we calculate the policy_logits and value for the leaf node (next_latent_state) by the prediction function. (aka. evaluation) MCTS stage 3: Backup At the end of the simulation, the statistics along the trajectory are updated. """ network_output = model.recurrent_inference( latent_states, (hidden_states_c_reward, hidden_states_h_reward), last_actions ) [ network_output.latent_state, network_output.policy_logits, network_output.value, network_output.value_prefix ] = to_detach_cpu_numpy( [ network_output.latent_state, network_output.policy_logits, self.inverse_scalar_transform_handle(network_output.value), self.inverse_scalar_transform_handle(network_output.value_prefix), ] ) network_output.reward_hidden_state = ( network_output.reward_hidden_state[0].detach().cpu().numpy(), network_output.reward_hidden_state[1].detach().cpu().numpy() ) latent_state_batch_in_search_path.append(network_output.latent_state) reward_latent_state_batch = network_output.reward_hidden_state # tolist() is to be compatible with cpp datatype. value_batch = network_output.value.reshape(-1).tolist() value_prefix_batch = network_output.value_prefix.reshape(-1).tolist() policy_logits_batch = network_output.policy_logits.tolist() # reset the hidden states in LSTM every ``lstm_horizon_len`` steps in one search. # which enable the model only need to predict the value prefix in a range (e.g.: [s0,...,s5]). assert self._cfg.lstm_horizon_len > 0 reset_idx = (np.array(search_lens) % self._cfg.lstm_horizon_len == 0) reward_latent_state_batch[0][:, reset_idx, :] = 0 reward_latent_state_batch[1][:, reset_idx, :] = 0 is_reset_list = reset_idx.astype(np.int32).tolist() reward_hidden_state_c_batch.append(reward_latent_state_batch[0]) reward_hidden_state_h_batch.append(reward_latent_state_batch[1]) # In ``batch_backpropagate()``, we first expand the leaf node using ``the policy_logits`` and # ``reward`` predicted by the model, then perform backpropagation along the search path to update the # statistics. # NOTE: simulation_index + 1 is very important, which is the depth of the current leaf node. current_latent_state_index = simulation_index + 1 tree_sez.batch_backpropagate( current_latent_state_index, discount_factor, value_prefix_batch, value_batch, policy_logits_batch, min_max_stats_lst, results, is_reset_list, virtual_to_play )