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from typing import List
from mlagents.torch_utils import torch
from mlagents_envs.base_env import ActionSpec
from mlagents.trainers.torch_entities.agent_action import AgentAction
from mlagents.trainers.torch_entities.utils import ModelUtils
class ActionFlattener:
def __init__(self, action_spec: ActionSpec):
"""
A torch module that creates the flattened form of an AgentAction object.
The flattened form is the continuous action concatenated with the
concatenated one hot encodings of the discrete actions.
:param action_spec: An ActionSpec that describes the action space dimensions
"""
self._specs = action_spec
@property
def flattened_size(self) -> int:
"""
The flattened size is the continuous size plus the sum of the branch sizes
since discrete actions are encoded as one hots.
"""
return self._specs.continuous_size + sum(self._specs.discrete_branches)
def forward(self, action: AgentAction) -> torch.Tensor:
"""
Returns a tensor corresponding the flattened action
:param action: An AgentAction object
"""
action_list: List[torch.Tensor] = []
if self._specs.continuous_size > 0:
action_list.append(action.continuous_tensor)
if self._specs.discrete_size > 0:
flat_discrete = torch.cat(
ModelUtils.actions_to_onehot(
torch.as_tensor(action.discrete_tensor, dtype=torch.long),
self._specs.discrete_branches,
),
dim=1,
)
action_list.append(flat_discrete)
return torch.cat(action_list, dim=1)
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