import copy
from collections import namedtuple
from typing import List, Dict, Tuple
import numpy as np
import torch.distributions
import torch.nn.functional as F
import torch.optim as optim
from ding.policy.base_policy import Policy
from ding.torch_utils import to_device
from ding.utils import POLICY_REGISTRY
from ding.utils.data import default_collate
from easydict import EasyDict
from lzero.policy import configure_optimizers
[docs]@POLICY_REGISTRY.register('alphazero')
class AlphaZeroPolicy(Policy):
"""
Overview:
The policy class for AlphaZero.
"""
# The default_config for AlphaZero policy.
config = dict(
# (bool) Whether to use torch.compile method to speed up our model, which required torch>=2.0.
torch_compile=False,
# (bool) Whether to use TF32 for our model.
tensor_float_32=False,
model=dict(
# (tuple) The stacked obs shape.
observation_shape=(3, 6, 6),
# (int) The number of res blocks in AlphaZero model.
num_res_blocks=1,
# (int) The number of channels of hidden states in AlphaZero model.
num_channels=32,
),
# (bool) Whether to enable the sampled-based algorithm (e.g. Sampled EfficientZero)
# this variable is used in ``collector``.
sampled_algo=False,
# (bool) Whether to enable the gumbel-based algorithm (e.g. Gumbel Muzero)
gumbel_algo=False,
# (bool) Whether to use multi-gpu training.
multi_gpu=False,
# (bool) Whether to use cuda for network.
cuda=False,
# (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 -> ...
# For different env, we have different episode_length,
# we usually set update_per_collect = collector_env_num * episode_length / batch_size * reuse_factor.
# If we set update_per_collect=None, we will set update_per_collect = collected_transitions_num * cfg.policy.replay_ratio automatically.
update_per_collect=None,
# (float) The ratio of the collected data used for training. Only effective when ``update_per_collect`` is not None.
replay_ratio=0.25,
# (int) Minibatch size for one gradient descent.
batch_size=256,
# (str) Optimizer for training policy network. ['SGD', 'Adam', 'AdamW']
optim_type='SGD',
# (float) Learning rate for training policy network. Initial lr for manually decay schedule.
learning_rate=0.2,
# (float) Weight decay for training policy network.
weight_decay=1e-4,
# (float) One-order Momentum in optimizer, which stabilizes the training process (gradient direction).
momentum=0.9,
# (float) The maximum constraint value of gradient norm clipping.
grad_clip_value=10,
# (float) The weight of value loss.
value_weight=1.0,
# (int) The number of environments used in collecting data.
collector_env_num=8,
# (int) The number of environments used in evaluating policy.
evaluator_env_num=3,
# (bool) Whether to use piecewise constant learning rate decay.
# i.e. lr: 0.2 -> 0.02 -> 0.002
piecewise_decay_lr_scheduler=True,
# (int) The number of final training iterations to control lr decay, which is only used for manually decay.
threshold_training_steps_for_final_lr=int(5e5),
# (bool) Whether to use manually temperature decay.
# i.e. temperature: 1 -> 0.5 -> 0.25
manual_temperature_decay=False,
# (int) The number of final training iterations to control temperature, which is only used for manually decay.
threshold_training_steps_for_final_temperature=int(1e5),
# (float) The fixed temperature value for MCTS action selection, which is used to control the exploration.
# The larger the value, the more exploration. This value is only used when manual_temperature_decay=False.
fixed_temperature_value=0.25,
mcts=dict(
# (int) The number of simulations to perform at each move.
num_simulations=50,
# (int) The maximum number of moves to make in a game.
max_moves=512, # for chess and shogi, 722 for Go.
# (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,
),
other=dict(replay_buffer=dict(
replay_buffer_size=int(1e6),
save_episode=False,
)),
)
[docs] def default_model(self) -> Tuple[str, List[str]]:
"""
Overview:
Return this algorithm default model setting for demonstration.
Returns:
- model_type (:obj:`str`): The model type used in this algorithm, which is registered in ModelRegistry.
- import_names (:obj:`List[str]`): The model class path list used in this algorithm.
"""
return 'AlphaZeroModel', ['lzero.model.alphazero_model']
[docs] def _init_learn(self) -> None:
assert self._cfg.optim_type in ['SGD', 'Adam', 'AdamW'], self._cfg.optim_type
if self._cfg.optim_type == 'SGD':
self._optimizer = optim.SGD(
self._model.parameters(),
lr=self._cfg.learning_rate,
momentum=self._cfg.momentum,
weight_decay=self._cfg.weight_decay,
)
elif self._cfg.optim_type == 'Adam':
self._optimizer = optim.Adam(
self._model.parameters(), lr=self._cfg.learning_rate, weight_decay=self._cfg.weight_decay
)
elif self._cfg.optim_type == 'AdamW':
self._optimizer = configure_optimizers(
model=self._model,
weight_decay=self._cfg.weight_decay,
learning_rate=self._cfg.learning_rate,
device_type=self._cfg.device
)
if self._cfg.piecewise_decay_lr_scheduler:
from torch.optim.lr_scheduler import LambdaLR
max_step = self._cfg.threshold_training_steps_for_final_lr
# NOTE: the 1, 0.1, 0.01 is the decay rate, not the lr.
lr_lambda = lambda step: 1 if step < max_step * 0.5 else (0.1 if step < max_step else 0.01) # noqa
self.lr_scheduler = LambdaLR(self._optimizer, lr_lambda=lr_lambda)
# Algorithm config
self._value_weight = self._cfg.value_weight
self._entropy_weight = self._cfg.entropy_weight
# Main and target models
self._learn_model = self._model
# TODO(pu): test the effect of torch 2.0
if self._cfg.torch_compile:
self._learn_model = torch.compile(self._learn_model)
[docs] def _forward_learn(self, inputs: Dict[str, torch.Tensor]) -> Dict[str, float]:
inputs = default_collate(inputs)
if self._cuda:
inputs = to_device(inputs, self._device)
self._learn_model.train()
state_batch = inputs['obs']['observation']
mcts_probs = inputs['probs']
reward = inputs['reward']
state_batch = state_batch.to(device=self._device, dtype=torch.float)
mcts_probs = mcts_probs.to(device=self._device, dtype=torch.float)
reward = reward.to(device=self._device, dtype=torch.float)
action_probs, values = self._learn_model.compute_policy_value(state_batch)
policy_log_probs = torch.log(action_probs)
# calculate policy entropy, for monitoring only
entropy = torch.mean(-torch.sum(action_probs * policy_log_probs, 1))
entropy_loss = -entropy
# ============
# policy loss
# ============
policy_loss = -torch.mean(torch.sum(mcts_probs * policy_log_probs, 1))
# ============
# value loss
# ============
value_loss = F.mse_loss(values.view(-1), reward)
total_loss = self._value_weight * value_loss + policy_loss + self._entropy_weight * entropy_loss
self._optimizer.zero_grad()
total_loss.backward()
if self._cfg.multi_gpu:
self.sync_gradients(self._learn_model)
total_grad_norm_before_clip = torch.nn.utils.clip_grad_norm_(
list(self._model.parameters()),
max_norm=self._cfg.grad_clip_value,
)
self._optimizer.step()
if self._cfg.piecewise_decay_lr_scheduler is True:
self.lr_scheduler.step()
# =============
# after update
# =============
return {
'cur_lr': self._optimizer.param_groups[0]['lr'],
'total_loss': total_loss.item(),
'policy_loss': policy_loss.item(),
'value_loss': value_loss.item(),
'entropy_loss': entropy_loss.item(),
'total_grad_norm_before_clip': total_grad_norm_before_clip.item(),
'collect_mcts_temperature': self.collect_mcts_temperature,
}
[docs] def _init_collect(self) -> None:
"""
Overview:
Collect mode init method. Called by ``self.__init__``. Initialize the collect model and MCTS utils.
"""
self._get_simulation_env()
self._collect_model = self._model
if self._cfg.mcts_ctree:
import sys
sys.path.append('/Users/your_user_name/code/LightZero/lzero/mcts/ctree/ctree_alphazero/build')
import mcts_alphazero
self._collect_mcts = mcts_alphazero.MCTS(self._cfg.mcts.max_moves, self._cfg.mcts.num_simulations,
self._cfg.mcts.pb_c_base,
self._cfg.mcts.pb_c_init, self._cfg.mcts.root_dirichlet_alpha,
self._cfg.mcts.root_noise_weight, self.simulate_env)
else:
if self._cfg.sampled_algo:
from lzero.mcts.ptree.ptree_az_sampled import MCTS
else:
from lzero.mcts.ptree.ptree_az import MCTS
self._collect_mcts = MCTS(self._cfg.mcts, self.simulate_env)
self.collect_mcts_temperature = 1
[docs] @torch.no_grad()
def _forward_collect(self, obs: Dict, temperature: float = 1) -> Dict[str, torch.Tensor]:
"""
Overview:
The forward function for collecting data in collect mode. Use real env to execute MCTS search.
Arguments:
- obs (:obj:`Dict`): The dict of obs, the key is env_id and the value is the \
corresponding obs in this timestep.
- temperature (:obj:`float`): The temperature for MCTS search.
Returns:
- output (:obj:`Dict[str, torch.Tensor]`): The dict of output, the key is env_id and the value is the \
the corresponding policy output in this timestep, including action, probs and so on.
"""
self.collect_mcts_temperature = temperature
ready_env_id = list(obs.keys())
init_state = {env_id: obs[env_id]['board'] for env_id in ready_env_id}
# If 'katago_game_state' is in the observation of the given environment ID, it's value is used.
# If it's not present (which will raise a KeyError), None is used instead.
# This approach is taken to maintain compatibility with the handling of 'katago' related parts of 'alphazero_mcts_ctree' in Go.
katago_game_state = {env_id: obs[env_id].get('katago_game_state', None) for env_id in ready_env_id}
start_player_index = {env_id: obs[env_id]['current_player_index'] for env_id in ready_env_id}
output = {}
self._policy_model = self._collect_model
for env_id in ready_env_id:
state_config_for_simulation_env_reset = EasyDict(dict(start_player_index=start_player_index[env_id],
init_state=init_state[env_id],
katago_policy_init=False,
katago_game_state=katago_game_state[env_id]))
action, mcts_probs = self._collect_mcts.get_next_action(state_config_for_simulation_env_reset, self._policy_value_fn, self.collect_mcts_temperature, True)
output[env_id] = {
'action': action,
'probs': mcts_probs,
}
return output
[docs] def _init_eval(self) -> None:
"""
Overview:
Evaluate mode init method. Called by ``self.__init__``. Initialize the eval model and MCTS utils.
"""
self._get_simulation_env()
if self._cfg.mcts_ctree:
import sys
sys.path.append('/Users/your_user_name/code/LightZero/lzero/mcts/ctree/ctree_alphazero/build')
import mcts_alphazero
# TODO(pu): how to set proper num_simulations for evaluation
self._eval_mcts = mcts_alphazero.MCTS(self._cfg.mcts.max_moves,
min(800, self._cfg.mcts.num_simulations * 4),
self._cfg.mcts.pb_c_base,
self._cfg.mcts.pb_c_init, self._cfg.mcts.root_dirichlet_alpha,
self._cfg.mcts.root_noise_weight, self.simulate_env)
else:
if self._cfg.sampled_algo:
from lzero.mcts.ptree.ptree_az_sampled import MCTS
else:
from lzero.mcts.ptree.ptree_az import MCTS
mcts_eval_config = copy.deepcopy(self._cfg.mcts)
# TODO(pu): how to set proper num_simulations for evaluation
mcts_eval_config.num_simulations = min(800, mcts_eval_config.num_simulations * 4)
self._eval_mcts = MCTS(mcts_eval_config, self.simulate_env)
self._eval_model = self._model
[docs] def _forward_eval(self, obs: Dict) -> Dict[str, torch.Tensor]:
"""
Overview:
The forward function for evaluating the current policy in eval mode, similar to ``self._forward_collect``.
Arguments:
- obs (:obj:`Dict`): The dict of obs, the key is env_id and the value is the \
corresponding obs in this timestep.
Returns:
- output (:obj:`Dict[str, torch.Tensor]`): The dict of output, the key is env_id and the value is the \
the corresponding policy output in this timestep, including action, probs and so on.
"""
ready_env_id = list(obs.keys())
init_state = {env_id: obs[env_id]['board'] for env_id in ready_env_id}
# If 'katago_game_state' is in the observation of the given environment ID, it's value is used.
# If it's not present (which will raise a KeyError), None is used instead.
# This approach is taken to maintain compatibility with the handling of 'katago' related parts of 'alphazero_mcts_ctree' in Go.
katago_game_state = {env_id: obs[env_id].get('katago_game_state', None) for env_id in ready_env_id}
start_player_index = {env_id: obs[env_id]['current_player_index'] for env_id in ready_env_id}
output = {}
self._policy_model = self._eval_model
for env_id in ready_env_id:
state_config_for_simulation_env_reset = EasyDict(dict(start_player_index=start_player_index[env_id],
init_state=init_state[env_id],
katago_policy_init=False,
katago_game_state=katago_game_state[env_id]))
action, mcts_probs = self._eval_mcts.get_next_action(
state_config_for_simulation_env_reset, self._policy_value_fn, 1.0, False
)
output[env_id] = {
'action': action,
'probs': mcts_probs,
}
return output
[docs] def _get_simulation_env(self):
if self._cfg.simulation_env_id == 'tictactoe':
from zoo.board_games.tictactoe.envs.tictactoe_env import TicTacToeEnv
if self._cfg.simulation_env_config_type == 'play_with_bot':
from zoo.board_games.tictactoe.config.tictactoe_alphazero_bot_mode_config import \
tictactoe_alphazero_config
elif self._cfg.simulation_env_config_type == 'self_play':
from zoo.board_games.tictactoe.config.tictactoe_alphazero_sp_mode_config import \
tictactoe_alphazero_config
else:
raise NotImplementedError
self.simulate_env = TicTacToeEnv(tictactoe_alphazero_config.env)
elif self._cfg.simulation_env_id == 'gomoku':
from zoo.board_games.gomoku.envs.gomoku_env import GomokuEnv
if self._cfg.simulation_env_config_type == 'play_with_bot':
from zoo.board_games.gomoku.config.gomoku_alphazero_bot_mode_config import gomoku_alphazero_config
elif self._cfg.simulation_env_config_type == 'self_play':
from zoo.board_games.gomoku.config.gomoku_alphazero_sp_mode_config import gomoku_alphazero_config
else:
raise NotImplementedError
self.simulate_env = GomokuEnv(gomoku_alphazero_config.env)
elif self._cfg.simulation_env_id == 'connect4':
from zoo.board_games.connect4.envs.connect4_env import Connect4Env
if self._cfg.simulation_env_config_type == 'play_with_bot':
from zoo.board_games.connect4.config.connect4_alphazero_bot_mode_config import connect4_alphazero_config
elif self._cfg.simulation_env_config_type == 'self_play':
from zoo.board_games.connect4.config.connect4_alphazero_sp_mode_config import connect4_alphazero_config
else:
raise NotImplementedError
self.simulate_env = Connect4Env(connect4_alphazero_config.env)
elif self._cfg.simulation_env_id == 'chess':
from zoo.board_games.chess.envs.chess_lightzero_env import ChessLightZeroEnv
if self._cfg.simulation_env_config_type == 'play_with_bot':
from zoo.board_games.chess.config.chess_alphazero_bot_mode_config import chess_alphazero_config
elif self._cfg.simulation_env_config_type == 'self_play':
from zoo.board_games.chess.config.chess_alphazero_sp_mode_config import chess_alphazero_config
else:
raise NotImplementedError
self.simulate_env = ChessLightZeroEnv(chess_alphazero_config.env)
elif self._cfg.simulation_env_id == 'dummy_any_game':
from zoo.board_games.tictactoe.envs.dummy_any_game_env import AnyGameEnv
if self._cfg.simulation_env_config_type == 'single_player_mode':
from zoo.board_games.tictactoe.config.dummy_any_game_alphazero_single_player_mode_config import \
dummy_any_game_alphazero_config
elif self._cfg.simulation_env_config_type == 'self_play':
from zoo.board_games.tictactoe.config.dummy_any_game_alphazero_self_play_mode_config import \
dummy_any_game_alphazero_config
else:
raise NotImplementedError
self.simulate_env = AnyGameEnv(dummy_any_game_alphazero_config.env)
else:
raise NotImplementedError
[docs] @torch.no_grad()
def _policy_value_fn(self, env: 'Env') -> Tuple[Dict[int, np.ndarray], float]: # noqa
legal_actions = env.legal_actions
current_state, current_state_scale = env.current_state()
current_state_scale = torch.from_numpy(current_state_scale).to(
device=self._device, dtype=torch.float
).unsqueeze(0)
with torch.no_grad():
action_probs, value = self._policy_model.compute_policy_value(current_state_scale)
action_probs_dict = dict(zip(legal_actions, action_probs.squeeze(0)[legal_actions].detach().cpu().numpy()))
return action_probs_dict, value.item()
[docs] def _monitor_vars_learn(self) -> List[str]:
"""
Overview:
Register the variables to be monitored in learn mode. The registered variables will be logged in
tensorboard according to the return value ``_forward_learn``.
"""
return super()._monitor_vars_learn() + [
'cur_lr', 'total_loss', 'policy_loss', 'value_loss', 'entropy_loss', 'total_grad_norm_before_clip',
'collect_mcts_temperature'
]
[docs] def _process_transition(self, obs: Dict, model_output: Dict[str, torch.Tensor], timestep: namedtuple) -> Dict:
"""
Overview:
Generate the dict type transition (one timestep) data from policy learning.
"""
return {
'obs': obs,
'next_obs': timestep.obs,
'action': model_output['action'],
'probs': model_output['probs'],
'reward': timestep.reward,
'done': timestep.done,
}
[docs] def _get_train_sample(self, data):
# be compatible with DI-engine Policy class
pass