mirror of
https://github.com/vale981/ray
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47 lines
1.5 KiB
Python
47 lines
1.5 KiB
Python
from typing import Union
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from ray.rllib.utils.annotations import PublicAPI
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from ray.rllib.models.action_dist import ActionDistribution
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from ray.rllib.utils.annotations import override
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from ray.rllib.utils.exploration.exploration import TensorType
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from ray.rllib.utils.exploration.soft_q import SoftQ
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from ray.rllib.utils.framework import try_import_tf, try_import_torch
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tf1, tf, tfv = try_import_tf()
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torch, _ = try_import_torch()
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@PublicAPI
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class SlateSoftQ(SoftQ):
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@override(SoftQ)
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def get_exploration_action(
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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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):
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assert (
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self.framework == "torch"
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), "ERROR: SlateSoftQ only supports torch so far!"
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cls = type(action_distribution)
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# Re-create the action distribution with the correct temperature
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# applied.
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action_distribution = cls(
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action_distribution.inputs, self.model, temperature=self.temperature
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)
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batch_size = action_distribution.inputs.size()[0]
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action_logp = torch.zeros(batch_size, dtype=torch.float)
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self.last_timestep = timestep
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# Explore.
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if explore:
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# Return stochastic sample over (q-value) logits.
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action = action_distribution.sample()
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# Return the deterministic "sample" (argmax) over (q-value) logits.
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else:
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action = action_distribution.deterministic_sample()
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return action, action_logp
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