a8a367c42d
* Add example atari_iqn_hl * Factor out trainer callbacks to new module atari_callbacks * Extract base class for DQN-based agent factories * Improved module factory interface design, achieving higher generality
69 lines
2.2 KiB
Python
69 lines
2.2 KiB
Python
from abc import ABC, abstractmethod
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from collections.abc import Sequence
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from dataclasses import dataclass
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from typing import TypeAlias
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import numpy as np
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import torch
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from tianshou.highlevel.env import Environments
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from tianshou.utils.net.discrete import ImplicitQuantileNetwork
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from tianshou.utils.string import ToStringMixin
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TDevice: TypeAlias = str | torch.device
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def init_linear_orthogonal(module: torch.nn.Module) -> None:
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"""Applies orthogonal initialization to linear layers of the given module and sets bias weights to 0.
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:param module: the module whose submodules are to be processed
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"""
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for m in module.modules():
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if isinstance(m, torch.nn.Linear):
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torch.nn.init.orthogonal_(m.weight, gain=np.sqrt(2))
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torch.nn.init.zeros_(m.bias)
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class ModuleFactory(ABC):
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@abstractmethod
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def create_module(self, envs: Environments, device: TDevice) -> torch.nn.Module:
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pass
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@dataclass
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class IntermediateModule:
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module: torch.nn.Module
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output_dim: int
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class IntermediateModuleFactory(ToStringMixin, ModuleFactory, ABC):
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@abstractmethod
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def create_intermediate_module(self, envs: Environments, device: TDevice) -> IntermediateModule:
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pass
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def create_module(self, envs: Environments, device: TDevice) -> torch.nn.Module:
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return self.create_intermediate_module(envs, device).module
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class ImplicitQuantileNetworkFactory(ModuleFactory, ToStringMixin):
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def __init__(
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self,
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preprocess_net_factory: IntermediateModuleFactory,
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hidden_sizes: Sequence[int] = (),
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num_cosines: int = 64,
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):
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self.preprocess_net_factory = preprocess_net_factory
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self.hidden_sizes = hidden_sizes
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self.num_cosines = num_cosines
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def create_module(self, envs: Environments, device: TDevice) -> ImplicitQuantileNetwork:
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preprocess_net = self.preprocess_net_factory.create_intermediate_module(envs, device)
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return ImplicitQuantileNetwork(
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preprocess_net=preprocess_net.module,
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action_shape=envs.get_action_shape(),
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hidden_sizes=self.hidden_sizes,
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num_cosines=self.num_cosines,
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preprocess_net_output_dim=preprocess_net.output_dim,
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device=device,
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).to(device)
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