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https://github.com/wassname/ray.git
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Richard Liaw
bc082e9a9e
* Code for Supporting Shared Models Running (with vnet modification) - needs to be tested for performance Summaries Small refactoring + generalized to more domains Small fix for jenkins Linting linting Addressing changes Addressing changes Update envs.py Addressing changes convnet Merge - new model final touches final linting Changing iterations back removed extra change changes for fast experimentation changes to enable a2c TEMP FOR DEBUGGING ContinuousActions - Still doesn't work InvertedPendulum trains with 8 workers - k=200 huber loss Maxes for InvertedPendulum-v1 - 16w,200steps temp: working with a2c Back to shared model more fixes small nit LSTM to shared models need to fix last_features tuning pong Best record for hitting 0 - with k=16,n=20 nit a2cremoval remove A2c reference and nits nit removed a2c vestiges removing a2c removing example.py Linting nit * Linting + Removing vestigal code * Final Touches * nits * rerun travis
93 lines
3.2 KiB
Python
93 lines
3.2 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.preprocessors import (
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NoPreprocessor, AtariRamPreprocessor, AtariPixelPreprocessor)
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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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dist_type (Optional[str]): Identifier of the action distribution.
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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=dict()):
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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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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, obs_shape):
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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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obs_shape (tuple): The shape of the env observation space.
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Returns:
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preprocessor (Preprocessor): Preprocessor for the env observations.
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"""
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ATARI_OBS_SHAPE = (210, 160, 3)
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ATARI_RAM_OBS_SHAPE = (128,)
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if obs_shape == ATARI_OBS_SHAPE:
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print("Assuming Atari pixel env, using AtariPixelPreprocessor.")
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return AtariPixelPreprocessor()
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elif obs_shape == ATARI_RAM_OBS_SHAPE:
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print("Assuming Atari ram env, using AtariRamPreprocessor.")
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return AtariRamPreprocessor()
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print("Non-atari env, not using any observation preprocessor.")
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return NoPreprocessor()
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