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https://github.com/vale981/ray
synced 2025-03-09 04:46:38 -04:00
60d4d5e1aa
* Remove all __future__ imports from RLlib. * Remove (object) again from tf_run_builder.py::TFRunBuilder. * Fix 2xLINT warnings. * Fix broken appo_policy import (must be appo_tf_policy) * Remove future imports from all other ray files (not just RLlib). * Remove future imports from all other ray files (not just RLlib). * Remove future import blocks that contain `unicode_literals` as well. Revert appo_tf_policy.py to appo_policy.py (belongs to another PR). * Add two empty lines before Schedule class. * Put back __future__ imports into determine_tests_to_run.py. Fails otherwise on a py2/print related error.
64 lines
2.3 KiB
Python
64 lines
2.3 KiB
Python
import unittest
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import ray
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from ray.rllib.agents.dqn import DQNTrainer
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from ray.rllib.agents.a3c import A3CTrainer
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from ray.rllib.agents.dqn.dqn_policy import _adjust_nstep
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from ray.tune.registry import register_env
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import gym
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class EvalTest(unittest.TestCase):
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def testDqnNStep(self):
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obs = [1, 2, 3, 4, 5, 6, 7]
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actions = ["a", "b", "a", "a", "a", "b", "a"]
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rewards = [10.0, 0.0, 100.0, 100.0, 100.0, 100.0, 100.0]
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new_obs = [2, 3, 4, 5, 6, 7, 8]
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dones = [0, 0, 0, 0, 0, 0, 1]
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_adjust_nstep(3, 0.9, obs, actions, rewards, new_obs, dones)
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self.assertEqual(obs, [1, 2, 3, 4, 5, 6, 7])
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self.assertEqual(actions, ["a", "b", "a", "a", "a", "b", "a"])
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self.assertEqual(new_obs, [4, 5, 6, 7, 8, 8, 8])
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self.assertEqual(dones, [0, 0, 0, 0, 1, 1, 1])
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self.assertEqual(rewards,
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[91.0, 171.0, 271.0, 271.0, 271.0, 190.0, 100.0])
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def testEvaluationOption(self):
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def env_creator(env_config):
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return gym.make("CartPole-v0")
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agent_classes = [DQNTrainer, A3CTrainer]
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for agent_cls in agent_classes:
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ray.init(object_store_memory=1000 * 1024 * 1024)
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register_env("CartPoleWrapped-v0", env_creator)
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agent = agent_cls(
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env="CartPoleWrapped-v0",
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config={
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"evaluation_interval": 2,
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"evaluation_num_episodes": 2,
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"evaluation_config": {
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"gamma": 0.98,
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"env_config": {
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"fake_arg": True
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}
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},
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})
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# Given evaluation_interval=2, r0, r2, r4 should not contain
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# evaluation metrics while r1, r3 should do.
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r0 = agent.train()
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r1 = agent.train()
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r2 = agent.train()
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r3 = agent.train()
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self.assertTrue("evaluation" in r1)
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self.assertTrue("evaluation" in r3)
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self.assertFalse("evaluation" in r0)
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self.assertFalse("evaluation" in r2)
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self.assertTrue("episode_reward_mean" in r1["evaluation"])
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self.assertNotEqual(r1["evaluation"], r3["evaluation"])
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ray.shutdown()
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if __name__ == "__main__":
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unittest.main(verbosity=2)
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