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52 lines
2.1 KiB
Python
52 lines
2.1 KiB
Python
from gym.spaces import Discrete, Space
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from typing import Union, Optional
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from ray.rllib.models.action_dist import ActionDistribution
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from ray.rllib.models.tf.tf_action_dist import Categorical
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from ray.rllib.models.torch.torch_action_dist import TorchCategorical
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from ray.rllib.utils.annotations import override
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from ray.rllib.utils.exploration.stochastic_sampling import StochasticSampling
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from ray.rllib.utils.framework import TensorType
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class SoftQ(StochasticSampling):
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"""Special case of StochasticSampling w/ Categorical and temperature param.
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Returns a stochastic sample from a Categorical parameterized by the model
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output divided by the temperature. Returns the argmax iff explore=False.
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"""
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def __init__(self,
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action_space: Space,
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*,
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framework: Optional[str],
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temperature: float = 1.0,
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**kwargs):
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"""Initializes a SoftQ Exploration object.
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Args:
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action_space (Space): The gym action space used by the environment.
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temperature (float): The temperature to divide model outputs by
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before creating the Categorical distribution to sample from.
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framework (str): One of None, "tf", "torch".
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"""
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assert isinstance(action_space, Discrete)
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super().__init__(action_space, framework=framework, **kwargs)
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self.temperature = temperature
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@override(StochasticSampling)
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def get_exploration_action(self,
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action_distribution: ActionDistribution,
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timestep: Union[int, TensorType],
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explore: bool = True):
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cls = type(action_distribution)
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assert cls in [Categorical, TorchCategorical]
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# Re-create the action distribution with the correct temperature
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# applied.
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dist = cls(
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action_distribution.inputs,
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self.model,
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temperature=self.temperature)
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# Delegate to super method.
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return super().get_exploration_action(
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action_distribution=dist, timestep=timestep, explore=explore)
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