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from typing import List, Dict, Any, Optional, Tuple, Union
import numpy as np
import torch
from ding.policy.base_policy import Policy
from ding.utils import POLICY_REGISTRY
from lzero.policy import InverseScalarTransform, select_action, ez_network_output_unpack, mz_network_output_unpack
@POLICY_REGISTRY.register('lightzero_random_policy')
class LightZeroRandomPolicy(Policy):
"""
Overview:
The policy class for LightZero RandomPolicy.
"""
def __init__(
self,
cfg: dict,
model: Optional[Union[type, torch.nn.Module]] = None,
enable_field: Optional[List[str]] = None,
action_space: Any = None,
):
if cfg.type == 'muzero':
from lzero.mcts import MuZeroMCTSCtree as MCTSCtree
from lzero.mcts import MuZeroMCTSPtree as MCTSPtree
elif cfg.type == 'efficientzero':
from lzero.mcts import EfficientZeroMCTSCtree as MCTSCtree
from lzero.mcts import EfficientZeroMCTSPtree as MCTSPtree
elif cfg.type == 'sampled_efficientzero':
from lzero.mcts import SampledEfficientZeroMCTSCtree as MCTSCtree
from lzero.mcts import SampledEfficientZeroMCTSPtree as MCTSPtree
else:
raise NotImplementedError("need to implement pipeline: {}".format(cfg.type))
self.MCTSCtree = MCTSCtree
self.MCTSPtree = MCTSPtree
self.action_space = action_space
super().__init__(cfg, model, enable_field)
def default_model(self) -> Tuple[str, List[str]]:
"""
Overview:
Return this algorithm default model setting.
Returns:
- model_info (:obj:`Tuple[str, List[str]]`): model name and model import_names.
- 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.
.. note::
The user can define and use customized network model but must obey the same interface definition indicated \
by import_names path. For EfficientZero, ``lzero.model.efficientzero_model.EfficientZeroModel``
"""
if self._cfg.model.model_type == "conv":
if self._cfg.type == 'efficientzero':
return 'EfficientZeroModel', ['lzero.model.efficientzero_model']
elif self._cfg.type == 'muzero':
return 'MuZeroModel', ['lzero.model.muzero_model']
elif self._cfg.type == 'sampled_efficientzero':
return 'SampledEfficientZeroModel', ['lzero.model.sampled_efficientzero_model']
else:
raise NotImplementedError("need to implement pipeline: {}".format(self._cfg.type))
elif self._cfg.model.model_type == "mlp":
if self._cfg.type == 'efficientzero':
return 'EfficientZeroModelMLP', ['lzero.model.efficientzero_model_mlp']
elif self._cfg.type == 'muzero':
return 'MuZeroModelMLP', ['lzero.model.muzero_model_mlp']
elif self._cfg.type == 'sampled_efficientzero':
return 'SampledEfficientZeroModelMLP', ['lzero.model.sampled_efficientzero_model_mlp']
else:
raise NotImplementedError("need to implement pipeline: {}".format(self._cfg.type))
def _init_collect(self) -> None:
"""
Overview:
Collect mode init method. Called by ``self.__init__``. Initialize the collect model and MCTS utils.
"""
self._collect_model = self._model
if self._cfg.mcts_ctree:
self._mcts_collect = self.MCTSCtree(self._cfg)
else:
self._mcts_collect = self.MCTSPtree(self._cfg)
self._collect_mcts_temperature = 1
self.collect_epsilon = 0.0
self.inverse_scalar_transform_handle = InverseScalarTransform(
self._cfg.model.support_scale, self._cfg.device, self._cfg.model.categorical_distribution
)
def _forward_collect(
self,
data: torch.Tensor,
action_mask: list = None,
temperature: float = 1,
to_play: List = [-1],
epsilon: float = 0.25,
ready_env_id: np.array = None,
) -> Dict:
"""
Overview:
The forward function for collecting data in collect mode. Use model to execute MCTS search.
Choosing the action through sampling during the collect mode.
Arguments:
- data (:obj:`torch.Tensor`): The input data, i.e. the observation.
- action_mask (:obj:`list`): The action mask, i.e. the action that cannot be selected.
- temperature (:obj:`float`): The temperature of the policy.
- to_play (:obj:`int`): The player to play.
- ready_env_id (:obj:`list`): The id of the env that is ready to collect.
Shape:
- data (:obj:`torch.Tensor`):
- For Atari, :math:`(N, C*S, H, W)`, where N is the number of collect_env, C is the number of channels, \
S is the number of stacked frames, H is the height of the image, W is the width of the image.
- For lunarlander, :math:`(N, O)`, where N is the number of collect_env, O is the observation space size.
- action_mask: :math:`(N, action_space_size)`, where N is the number of collect_env.
- temperature: :math:`(1, )`.
- to_play: :math:`(N, 1)`, where N is the number of collect_env.
- ready_env_id: None
Returns:
- output (:obj:`Dict[int, Any]`): Dict type data, the keys including ``action``, ``distributions``, \
``visit_count_distribution_entropy``, ``value``, ``pred_value``, ``policy_logits``.
"""
self._collect_model.eval()
self._collect_mcts_temperature = temperature
active_collect_env_num = data.shape[0]
with torch.no_grad():
# data shape [B, S x C, W, H], e.g. {Tensor:(B, 12, 96, 96)}
network_output = self._collect_model.initial_inference(data)
if self._cfg.type in ['efficientzero', 'sampled_efficientzero']:
latent_state_roots, value_prefix_roots, reward_hidden_state_roots, pred_values, policy_logits = ez_network_output_unpack(
network_output
)
elif self._cfg.type == 'muzero':
latent_state_roots, reward_roots, pred_values, policy_logits = mz_network_output_unpack(network_output)
else:
raise NotImplementedError("need to implement pipeline: {}".format(self._cfg.type))
pred_values = self.inverse_scalar_transform_handle(pred_values).detach().cpu().numpy()
latent_state_roots = latent_state_roots.detach().cpu().numpy()
if self._cfg.type in ['efficientzero', 'sampled_efficientzero']:
reward_hidden_state_roots = (
reward_hidden_state_roots[0].detach().cpu().numpy(),
reward_hidden_state_roots[1].detach().cpu().numpy()
)
policy_logits = policy_logits.detach().cpu().numpy().tolist()
if self._cfg.model.continuous_action_space:
# when the action space of the environment is continuous, action_mask[:] is None.
# NOTE: in continuous action space env: we set all legal_actions as -1
legal_actions = [
[-1 for _ in range(self._cfg.model.num_of_sampled_actions)] for _ in range(active_collect_env_num)
]
else:
legal_actions = [
[i for i, x in enumerate(action_mask[j]) if x == 1] for j in range(active_collect_env_num)
]
# the only difference between collect and eval is the dirichlet noise.
if self._cfg.type in ['sampled_efficientzero']:
noises = [
np.random.dirichlet([self._cfg.root_dirichlet_alpha] * int(self._cfg.model.num_of_sampled_actions)
).astype(np.float32).tolist() for j in range(active_collect_env_num)
]
else:
noises = [
np.random.dirichlet([self._cfg.root_dirichlet_alpha] * int(sum(action_mask[j]))
).astype(np.float32).tolist() for j in range(active_collect_env_num)
]
if self._cfg.mcts_ctree:
# cpp mcts_tree
if self._cfg.type in ['sampled_efficientzero']:
roots = self.MCTSCtree.roots(
active_collect_env_num, legal_actions, self._cfg.model.action_space_size,
self._cfg.model.num_of_sampled_actions, self._cfg.model.continuous_action_space
)
else:
roots = self.MCTSCtree.roots(active_collect_env_num, legal_actions)
else:
# python mcts_tree
if self._cfg.type in ['sampled_efficientzero']:
roots = self.MCTSPtree.roots(
active_collect_env_num, legal_actions, self._cfg.model.action_space_size,
self._cfg.model.num_of_sampled_actions, self._cfg.model.continuous_action_space
)
else:
roots = self.MCTSPtree.roots(active_collect_env_num, legal_actions)
if self._cfg.type in ['efficientzero', 'sampled_efficientzero']:
roots.prepare(self._cfg.root_noise_weight, noises, value_prefix_roots, policy_logits, to_play)
self._mcts_collect.search(
roots, self._collect_model, latent_state_roots, reward_hidden_state_roots, to_play
)
elif self._cfg.type == 'muzero':
roots.prepare(self._cfg.root_noise_weight, noises, reward_roots, policy_logits, to_play)
self._mcts_collect.search(roots, self._collect_model, latent_state_roots, to_play)
else:
raise NotImplementedError("need to implement pipeline: {}".format(self._cfg.type))
roots_visit_count_distributions = roots.get_distributions()
roots_values = roots.get_values() # shape: {list: batch_size}
if self._cfg.type in ['sampled_efficientzero']:
roots_sampled_actions = roots.get_sampled_actions()
data_id = [i for i in range(active_collect_env_num)]
output = {i: None for i in data_id}
if ready_env_id is None:
ready_env_id = np.arange(active_collect_env_num)
for i, env_id in enumerate(ready_env_id):
distributions, value = roots_visit_count_distributions[i], roots_values[i]
if self._cfg.type in ['sampled_efficientzero']:
if self._cfg.mcts_ctree:
# In ctree, the method roots.get_sampled_actions() returns a list object.
root_sampled_actions = np.array([action for action in roots_sampled_actions[i]])
else:
# In ptree, the same method roots.get_sampled_actions() returns an Action object.
root_sampled_actions = np.array([action.value for action in roots_sampled_actions[i]])
# NOTE: Only legal actions possess visit counts, so the ``action_index_in_legal_action_set`` represents
# the index within the legal action set, rather than the index in the entire action set.
action_index_in_legal_action_set, visit_count_distribution_entropy = select_action(
distributions, temperature=self._collect_mcts_temperature, deterministic=False
)
# ****************************************************************
# NOTE: The action is randomly selected from the legal action set,
# the distribution is the real visit count distribution from the MCTS search.
if self._cfg.type in ['sampled_efficientzero']:
# ****** sample a random action from the legal action set ********
random_action = self.action_space.sample()
output[env_id] = {
'action': random_action,
'visit_count_distributions': distributions,
'root_sampled_actions': root_sampled_actions,
'visit_count_distribution_entropy': visit_count_distribution_entropy,
'searched_value': value,
'predicted_value': pred_values[i],
'predicted_policy_logits': policy_logits[i],
}
else:
# ****** sample a random action from the legal action set ********
random_action = int(np.random.choice(legal_actions[env_id], 1))
# all items except action are formally obtained from MCTS
output[env_id] = {
'action': random_action,
'visit_count_distributions': distributions,
'visit_count_distribution_entropy': visit_count_distribution_entropy,
'searched_value': value,
'predicted_value': pred_values[i],
'predicted_policy_logits': policy_logits[i],
}
return output
def _init_eval(self) -> None:
"""
Overview:
Evaluate mode init method. Called by ``self.__init__``. Initialize the eval model and MCTS utils.
"""
self._eval_model = self._model
if self._cfg.mcts_ctree:
self._mcts_eval = self.MCTSCtree(self._cfg)
else:
self._mcts_eval = self.MCTSPtree(self._cfg)
# be compatible with DI-engine Policy class
def _init_learn(self) -> None:
pass
def _forward_learn(self, data: torch.Tensor) -> Dict[str, Union[float, int]]:
pass
def _forward_eval(self, data: torch.Tensor, action_mask: list, to_play: -1, ready_env_id: np.array = None,):
pass
def _monitor_vars_learn(self) -> List[str]:
pass
def _state_dict_learn(self) -> Dict[str, Any]:
pass
def _load_state_dict_learn(self, state_dict: Dict[str, Any]) -> None:
pass
def _process_transition(self, obs, policy_output, timestep):
pass
def _get_train_sample(self, data):
pass
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