mirror of
https://github.com/vale981/ray
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92 lines
3.2 KiB
Python
92 lines
3.2 KiB
Python
import unittest
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import ray
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import ray.rllib.agents.a3c as a3c
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from ray.rllib.policy.sample_batch import DEFAULT_POLICY_ID
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from ray.rllib.utils.metrics.learner_info import LEARNER_INFO, \
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LEARNER_STATS_KEY
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from ray.rllib.utils.test_utils import check_compute_single_action, \
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check_train_results, framework_iterator
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class TestA3C(unittest.TestCase):
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"""Sanity tests for A2C exec impl."""
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def setUp(self):
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ray.init(num_cpus=4)
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def tearDown(self):
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ray.shutdown()
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def test_a3c_compilation(self):
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"""Test whether an A3CTrainer can be built with both frameworks."""
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config = a3c.DEFAULT_CONFIG.copy()
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config["num_workers"] = 2
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config["num_envs_per_worker"] = 2
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num_iterations = 1
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# Test against all frameworks.
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for _ in framework_iterator(config, with_eager_tracing=True):
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for env in ["CartPole-v1", "Pendulum-v1", "PongDeterministic-v0"]:
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print("env={}".format(env))
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config["model"]["use_lstm"] = env == "CartPole-v1"
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trainer = a3c.A3CTrainer(config=config, env=env)
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for i in range(num_iterations):
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results = trainer.train()
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check_train_results(results)
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print(results)
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check_compute_single_action(
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trainer, include_state=config["model"]["use_lstm"])
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trainer.stop()
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def test_a3c_entropy_coeff_schedule(self):
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"""Test A3CTrainer entropy coeff schedule support."""
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config = a3c.DEFAULT_CONFIG.copy()
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config["num_workers"] = 1
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config["num_envs_per_worker"] = 1
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config["train_batch_size"] = 20
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config["batch_mode"] = "truncate_episodes"
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config["rollout_fragment_length"] = 10
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config["timesteps_per_iteration"] = 20
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# 0 metrics reporting delay, this makes sure timestep,
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# which entropy coeff depends on, is updated after each worker rollout.
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config["min_iter_time_s"] = 0
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# Initial lr, doesn't really matter because of the schedule below.
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config["entropy_coeff"] = 0.01
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schedule = [
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[0, 0.01],
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[120, 0.0001],
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]
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config["entropy_coeff_schedule"] = schedule
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def _step_n_times(trainer, n: int):
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"""Step trainer n times.
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Returns:
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learning rate at the end of the execution.
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"""
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for _ in range(n):
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results = trainer.train()
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return results["info"][LEARNER_INFO][DEFAULT_POLICY_ID][
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LEARNER_STATS_KEY]["entropy_coeff"]
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# Test against all frameworks.
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for _ in framework_iterator(config):
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trainer = a3c.A3CTrainer(config=config, env="CartPole-v1")
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coeff = _step_n_times(trainer, 1) # 20 timesteps
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# Should be close to the starting coeff of 0.01
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self.assertGreaterEqual(coeff, 0.005)
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coeff = _step_n_times(trainer, 10) # 200 timesteps
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# Should have annealed to the final coeff of 0.0001.
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self.assertLessEqual(coeff, 0.00011)
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trainer.stop()
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if __name__ == "__main__":
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import pytest
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import sys
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sys.exit(pytest.main(["-v", __file__]))
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