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
77 lines
2.5 KiB
Python
77 lines
2.5 KiB
Python
from abc import ABC, abstractmethod
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from collections.abc import Sequence
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from typing import Generic, TypeVar
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from tianshou.highlevel.env import Environments
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from tianshou.highlevel.module.core import IntermediateModuleFactory, TDevice
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from tianshou.highlevel.optim import OptimizerFactory
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from tianshou.policy import BasePolicy, ICMPolicy
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from tianshou.utils.net.discrete import IntrinsicCuriosityModule
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from tianshou.utils.string import ToStringMixin
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TPolicyIn = TypeVar("TPolicyIn", bound=BasePolicy)
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TPolicyOut = TypeVar("TPolicyOut", bound=BasePolicy)
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class PolicyWrapperFactory(Generic[TPolicyIn, TPolicyOut], ToStringMixin, ABC):
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@abstractmethod
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def create_wrapped_policy(
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self,
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policy: TPolicyIn,
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envs: Environments,
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optim_factory: OptimizerFactory,
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device: TDevice,
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) -> TPolicyOut:
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pass
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class PolicyWrapperFactoryIntrinsicCuriosity(
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Generic[TPolicyIn],
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PolicyWrapperFactory[TPolicyIn, ICMPolicy],
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):
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def __init__(
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self,
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feature_net_factory: IntermediateModuleFactory,
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hidden_sizes: Sequence[int],
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lr: float,
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lr_scale: float,
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reward_scale: float,
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forward_loss_weight: float,
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):
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self.feature_net_factory = feature_net_factory
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self.hidden_sizes = hidden_sizes
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self.lr = lr
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self.lr_scale = lr_scale
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self.reward_scale = reward_scale
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self.forward_loss_weight = forward_loss_weight
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def create_wrapped_policy(
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self,
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policy: TPolicyIn,
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envs: Environments,
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optim_factory: OptimizerFactory,
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device: TDevice,
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) -> ICMPolicy:
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feature_net = self.feature_net_factory.create_intermediate_module(envs, device)
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action_dim = envs.get_action_shape()
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if not isinstance(action_dim, int):
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raise ValueError(f"Environment action shape must be an integer, got {action_dim}")
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feature_dim = feature_net.output_dim
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icm_net = IntrinsicCuriosityModule(
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feature_net.module,
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feature_dim,
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action_dim,
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hidden_sizes=self.hidden_sizes,
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device=device,
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)
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icm_optim = optim_factory.create_optimizer(icm_net, lr=self.lr)
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return ICMPolicy(
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policy=policy,
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model=icm_net,
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optim=icm_optim,
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action_space=envs.get_action_space(),
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lr_scale=self.lr_scale,
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reward_scale=self.reward_scale,
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forward_loss_weight=self.forward_loss_weight,
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).to(device)
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