mirror of
https://github.com/wassname/ray.git
synced 2026-07-03 14:02:08 +08:00
Eric Liang
fbe6c59f72
A bunch of minor rllib fixes: pull in latest baselines atari wrapper changes (and use deepmind wrapper by default) move reward clipping to policy evaluator add a2c variant of a3c reduce vision network fc layer size to 256 units switch to 84x84 images doc tweaks print timesteps in tune status
69 lines
2.0 KiB
Python
69 lines
2.0 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 tensorflow as tf
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import tensorflow.contrib.slim as slim
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from ray.rllib.models.model import Model
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from ray.rllib.models.misc import get_activation_fn, flatten
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class VisionNetwork(Model):
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"""Generic vision network."""
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def _build_layers(self, inputs, num_outputs, options):
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filters = options.get("conv_filters")
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if not filters:
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filters = get_filter_config(options)
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activation = get_activation_fn(options.get("conv_activation", "relu"))
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with tf.name_scope("vision_net"):
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for i, (out_size, kernel, stride) in enumerate(filters[:-1], 1):
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inputs = slim.conv2d(
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inputs,
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out_size,
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kernel,
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stride,
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activation_fn=activation,
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scope="conv{}".format(i))
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out_size, kernel, stride = filters[-1]
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fc1 = slim.conv2d(
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inputs,
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out_size,
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kernel,
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stride,
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activation_fn=activation,
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padding="VALID",
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scope="fc1")
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fc2 = slim.conv2d(
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fc1,
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num_outputs, [1, 1],
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activation_fn=None,
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normalizer_fn=None,
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scope="fc2")
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return flatten(fc2), flatten(fc1)
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def get_filter_config(options):
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filters_84x84 = [
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[16, [8, 8], 4],
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[32, [4, 4], 2],
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[256, [11, 11], 1],
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]
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filters_42x42 = [
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[16, [4, 4], 2],
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[32, [4, 4], 2],
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[256, [11, 11], 1],
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]
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dim = options.get("dim", 84)
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if dim == 84:
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return filters_84x84
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elif dim == 42:
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return filters_42x42
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else:
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raise ValueError(
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"No default configuration for image size={}".format(dim) +
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", you must specify `conv_filters` manually as a model option.")
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