mirror of
https://github.com/vale981/ray
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105 lines
4.2 KiB
Python
105 lines
4.2 KiB
Python
from typing import Union
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from ray.rllib.utils.annotations import override
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from ray.rllib.utils.exploration.exploration import Exploration
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from ray.rllib.utils.framework import try_import_tf, try_import_torch, \
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TensorType
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from ray.rllib.utils.tuple_actions import TupleActions
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from ray.rllib.models.modelv2 import ModelV2
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tf = try_import_tf()
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torch, _ = try_import_torch()
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class StochasticSampling(Exploration):
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"""An exploration that simply samples from a distribution.
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The sampling can be made deterministic by passing explore=False into
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the call to `get_exploration_action`.
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Also allows for scheduled parameters for the distributions, such as
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lowering stddev, temperature, etc.. over time.
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"""
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def __init__(self,
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action_space,
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*,
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static_params=None,
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time_dependent_params=None,
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framework="tf",
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**kwargs):
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"""Initializes a StochasticSampling 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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static_params (Optional[dict]): Parameters to be passed as-is into
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the action distribution class' constructor.
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time_dependent_params (dict): Parameters to be evaluated based on
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`timestep` and then passed into the action distribution
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class' constructor.
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framework (Optional[str]): One of None, "tf", "torch".
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"""
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assert framework is not None
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super().__init__(action_space, framework=framework, **kwargs)
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self.static_params = static_params or {}
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# TODO(sven): Support scheduled params whose values depend on timestep
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# and that will be passed into the distribution's c'tor.
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self.time_dependent_params = time_dependent_params or {}
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@override(Exploration)
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def get_exploration_action(self,
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distribution_inputs: TensorType,
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action_dist_class: type,
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model: ModelV2,
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timestep: Union[int, TensorType],
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explore: bool = True):
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kwargs = self.static_params.copy()
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# TODO(sven): create schedules for these via easy-config patterns
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# These can be used anywhere in configs, where schedules are wanted:
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# e.g. lr=[0.003, 0.00001, 100k] <- linear anneal from 0.003, to
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# 0.00001 over 100k ts.
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# if self.time_dependent_params:
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# for k, v in self.time_dependent_params:
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# kwargs[k] = v(timestep)
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action_dist = action_dist_class(distribution_inputs, model, **kwargs)
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if self.framework == "torch":
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return self._get_torch_exploration_action(action_dist, explore)
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else:
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return self._get_tf_exploration_action_op(action_dist, explore)
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def _get_tf_exploration_action_op(self, action_dist, explore):
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sample = action_dist.sample()
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deterministic_sample = action_dist.deterministic_sample()
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action = tf.cond(
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tf.constant(explore) if isinstance(explore, bool) else explore,
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true_fn=lambda: sample,
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false_fn=lambda: deterministic_sample)
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def logp_false_fn():
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# TODO(sven): Move into (deterministic_)sample(logp=True|False)
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if isinstance(sample, TupleActions):
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batch_size = tf.shape(action[0])[0]
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else:
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batch_size = tf.shape(action)[0]
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return tf.zeros(shape=(batch_size, ), dtype=tf.float32)
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logp = tf.cond(
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tf.constant(explore) if isinstance(explore, bool) else explore,
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true_fn=lambda: action_dist.sampled_action_logp(),
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false_fn=logp_false_fn)
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return TupleActions(action) if isinstance(sample, TupleActions) \
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else action, logp
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@staticmethod
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def _get_torch_exploration_action(action_dist, explore):
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if explore:
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action = action_dist.sample()
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logp = action_dist.sampled_action_logp()
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else:
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action = action_dist.deterministic_sample()
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logp = torch.zeros((action.size()[0], ), dtype=torch.float32)
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return action, logp
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