mirror of
https://github.com/vale981/ray
synced 2025-03-09 21:06:39 -04:00
e3c9f7e83a
* custom action dist wip * Test case for custom action dist * ActionDistribution.get_parameter_shape_for_action_space pattern * Edit exception message to also suggest using a custom action distribution * Clean up ModelCatalog.get_action_dist * Pass model config to ActionDistribution constructors * Update custom action distribution test case * Name fix * Autoformatter * parameter shape static methods for torch distributions * Fix docstring * Generalize fake array for graph initialization * Fix action dist constructors * Correct parameter shape static methods for multicategorical and gaussian * Make suggested changes to custom action dist's * Correct instances of not passing model config to action dist * Autoformatter * fix tuple distribution constructor * bugfix
64 lines
2.3 KiB
Python
64 lines
2.3 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 unittest
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import numpy as np
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from numpy.testing import assert_allclose
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from ray.rllib.models.tf.tf_action_dist import Categorical
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from ray.rllib.agents.ppo.utils import flatten, concatenate
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from ray.rllib.utils import try_import_tf
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tf = try_import_tf()
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# TODO(ekl): move to rllib/models dir
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class DistributionsTest(unittest.TestCase):
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def testCategorical(self):
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num_samples = 100000
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logits = tf.placeholder(tf.float32, shape=(None, 10))
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z = 8 * (np.random.rand(10) - 0.5)
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data = np.tile(z, (num_samples, 1))
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c = Categorical(logits, {}) # dummy config dict
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sample_op = c.sample()
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sess = tf.Session()
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sess.run(tf.global_variables_initializer())
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samples = sess.run(sample_op, feed_dict={logits: data})
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counts = np.zeros(10)
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for sample in samples:
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counts[sample] += 1.0
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probs = np.exp(z) / np.sum(np.exp(z))
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self.assertTrue(np.sum(np.abs(probs - counts / num_samples)) <= 0.01)
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class UtilsTest(unittest.TestCase):
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def testFlatten(self):
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d = {
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"s": np.array([[[1, -1], [2, -2]], [[3, -3], [4, -4]]]),
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"a": np.array([[[5], [-5]], [[6], [-6]]])
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}
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flat = flatten(d.copy(), start=0, stop=2)
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assert_allclose(d["s"][0][0][:], flat["s"][0][:])
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assert_allclose(d["s"][0][1][:], flat["s"][1][:])
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assert_allclose(d["s"][1][0][:], flat["s"][2][:])
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assert_allclose(d["s"][1][1][:], flat["s"][3][:])
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assert_allclose(d["a"][0][0], flat["a"][0])
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assert_allclose(d["a"][0][1], flat["a"][1])
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assert_allclose(d["a"][1][0], flat["a"][2])
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assert_allclose(d["a"][1][1], flat["a"][3])
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def testConcatenate(self):
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d1 = {"s": np.array([0, 1]), "a": np.array([2, 3])}
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d2 = {"s": np.array([4, 5]), "a": np.array([6, 7])}
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d = concatenate([d1, d2])
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assert_allclose(d["s"], np.array([0, 1, 4, 5]))
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assert_allclose(d["a"], np.array([2, 3, 6, 7]))
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D = concatenate([d])
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assert_allclose(D["s"], np.array([0, 1, 4, 5]))
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assert_allclose(D["a"], np.array([2, 3, 6, 7]))
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if __name__ == "__main__":
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unittest.main(verbosity=2)
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