1cba589bd4
* Allow to specify trainer callbacks (train_fn, test_fn, stop_fn) in high-level API, adding the necessary abstractions and pass-on mechanisms * Add example atari_dqn_hl
48 lines
1.4 KiB
Python
48 lines
1.4 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.common import Net
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from tianshou.utils.string import ToStringMixin
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TDevice: TypeAlias = str | int | torch.device
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def init_linear_orthogonal(module: torch.nn.Module):
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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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@dataclass
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class Module:
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module: torch.nn.Module
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output_dim: int
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class ModuleFactory(ToStringMixin, ABC):
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@abstractmethod
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def create_module(self, envs: Environments, device: TDevice) -> Module:
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pass
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class ModuleFactoryNet(
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ModuleFactory,
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): # TODO This is unused and broken; use it in ActorFactory* and so on?
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def __init__(self, hidden_sizes: int | Sequence[int]):
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self.hidden_sizes = hidden_sizes
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def create_module(self, envs: Environments, device: TDevice) -> Module:
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module = Net(envs.get_observation_shape())
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return Module(module, module.output_dim)
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