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from typing import Tuple, Type
import gym
import torch.nn as nn
from gym.spaces import Box, Discrete, MultiDiscrete
from rl_algo_impls.shared.actor.actor import Actor
from rl_algo_impls.shared.actor.categorical import CategoricalActorHead
from rl_algo_impls.shared.actor.gaussian import GaussianActorHead
from rl_algo_impls.shared.actor.gridnet import GridnetActorHead
from rl_algo_impls.shared.actor.gridnet_decoder import GridnetDecoder
from rl_algo_impls.shared.actor.multi_discrete import MultiDiscreteActorHead
from rl_algo_impls.shared.actor.state_dependent_noise import (
StateDependentNoiseActorHead,
)
from rl_algo_impls.shared.encoder import EncoderOutDim
def actor_head(
action_space: gym.Space,
in_dim: EncoderOutDim,
hidden_sizes: Tuple[int, ...],
init_layers_orthogonal: bool,
activation: Type[nn.Module],
log_std_init: float = -0.5,
use_sde: bool = False,
full_std: bool = True,
squash_output: bool = False,
actor_head_style: str = "single",
) -> Actor:
assert not use_sde or isinstance(
action_space, Box
), "use_sde only valid if Box action_space"
assert not squash_output or use_sde, "squash_output only valid if use_sde"
if isinstance(action_space, Discrete):
assert isinstance(in_dim, int)
return CategoricalActorHead(
action_space.n, # type: ignore
in_dim=in_dim,
hidden_sizes=hidden_sizes,
activation=activation,
init_layers_orthogonal=init_layers_orthogonal,
)
elif isinstance(action_space, Box):
assert isinstance(in_dim, int)
if use_sde:
return StateDependentNoiseActorHead(
action_space.shape[0], # type: ignore
in_dim=in_dim,
hidden_sizes=hidden_sizes,
activation=activation,
init_layers_orthogonal=init_layers_orthogonal,
log_std_init=log_std_init,
full_std=full_std,
squash_output=squash_output,
)
else:
return GaussianActorHead(
action_space.shape[0], # type: ignore
in_dim=in_dim,
hidden_sizes=hidden_sizes,
activation=activation,
init_layers_orthogonal=init_layers_orthogonal,
log_std_init=log_std_init,
)
elif isinstance(action_space, MultiDiscrete):
if actor_head_style == "single":
return MultiDiscreteActorHead(
action_space.nvec, # type: ignore
in_dim=in_dim,
hidden_sizes=hidden_sizes,
activation=activation,
init_layers_orthogonal=init_layers_orthogonal,
)
elif actor_head_style == "gridnet":
return GridnetActorHead(
action_space.nvec[0], # type: ignore
action_space.nvec[1:], # type: ignore
in_dim=in_dim,
hidden_sizes=hidden_sizes,
activation=activation,
init_layers_orthogonal=init_layers_orthogonal,
)
elif actor_head_style == "gridnet_decoder":
return GridnetDecoder(
action_space.nvec[0], # type: ignore
action_space.nvec[1:], # type: ignore
in_dim=in_dim,
activation=activation,
init_layers_orthogonal=init_layers_orthogonal,
)
else:
raise ValueError(f"Doesn't support actor_head_style {actor_head_style}")
else:
raise ValueError(f"Unsupported action space: {action_space}")
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