mirror of
https://github.com/wassname/ray.git
synced 2026-07-03 17:32:29 +08:00
Eric Liang
b6a18cb39b
* wip * works with cartpole * lint * fix pg * comment * action dist rename * preprocessor * fix test * typo * fix the action[0] nonsense * revert * satisfy the lint * wip * works with cartpole * lint * fix pg * comment * action dist rename * preprocessor * fix test * typo * fix the action[0] nonsense * revert * satisfy the lint * Minor indentation changes. * fix merge * add humanoid * initial dqn refactor * remove tfutil * fix calls * fix tf errors 1 * closer * runs now * lint * tensorboard graph * fix linting * more 4 space * fix * fix linT * more lint * oops * es parity * remove example.py * fix training bug * add cartpole demo * try fixing cartpole * allow model options, configure cartpole * debug * simplify * no dueling * avoid out of file handles * Test dqn in jenkins. * Minor formatting. * fix issue * fix another * Fix problem in which we log to a directory that hasn't been created.
81 lines
2.5 KiB
Python
81 lines
2.5 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import gym
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from ray.rllib.models.action_dist import (
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Categorical, Deterministic, DiagGaussian)
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from ray.rllib.models.fcnet import FullyConnectedNetwork
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from ray.rllib.models.visionnet import VisionNetwork
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class ModelCatalog(object):
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"""Registry of default models and action distributions for envs.
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Example:
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dist_class, dist_dim = ModelCatalog.get_action_dist(env.action_space)
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model = ModelCatalog.get_model(inputs, dist_dim)
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dist = dist_class(model.outputs)
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action_op = dist.sample()
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"""
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@staticmethod
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def get_action_dist(action_space, dist_type=None):
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"""Returns action distribution class and size for the given action space.
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Args:
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action_space (Space): Action space of the target gym env.
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Returns:
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dist_class (ActionDistribution): Python class of the distribution.
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dist_dim (int): The size of the input vector to the distribution.
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"""
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if isinstance(action_space, gym.spaces.Box):
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if dist_type is None:
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return DiagGaussian, action_space.shape[0] * 2
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elif dist_type == 'deterministic':
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return Deterministic, action_space.shape[0]
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elif isinstance(action_space, gym.spaces.Discrete):
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return Categorical, action_space.n
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raise NotImplementedError(
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"Unsupported args: {} {}".format(action_space, dist_type))
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@staticmethod
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def get_model(inputs, num_outputs, options=None):
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"""Returns a suitable model conforming to given input and output specs.
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Args:
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inputs (Tensor): The input tensor to the model.
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num_outputs (int): The size of the output vector of the model.
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options (dict): Optional args to pass to the model constructor.
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Returns:
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model (Model): Neural network model.
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"""
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if options is None:
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options = {}
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obs_rank = len(inputs.get_shape()) - 1
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if obs_rank > 1:
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return VisionNetwork(inputs, num_outputs, options)
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return FullyConnectedNetwork(inputs, num_outputs, options)
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@staticmethod
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def get_preprocessor(env_name):
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"""Returns a suitable processor for the given environment.
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Args:
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env_name (str): The name of the environment.
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Returns:
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preprocessor (Preprocessor): Preprocessor for the env observations.
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"""
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raise NotImplementedError
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