mirror of
https://github.com/vale981/ray
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60 lines
2.1 KiB
Python
60 lines
2.1 KiB
Python
import pytest
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import unittest
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import ray
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import ray.rllib.agents.ddpg.apex as apex_ddpg
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from ray.rllib.utils.test_utils import check, check_compute_single_action, \
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check_train_results, framework_iterator
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class TestApexDDPG(unittest.TestCase):
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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_apex_ddpg_compilation_and_per_worker_epsilon_values(self):
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"""Test whether an APEX-DDPGTrainer can be built on all frameworks."""
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config = apex_ddpg.APEX_DDPG_DEFAULT_CONFIG.copy()
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config["num_workers"] = 2
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config["prioritized_replay"] = True
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config["timesteps_per_iteration"] = 100
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config["min_iter_time_s"] = 1
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config["learning_starts"] = 0
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config["optimizer"]["num_replay_buffer_shards"] = 1
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num_iterations = 1
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for _ in framework_iterator(config, with_eager_tracing=True):
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plain_config = config.copy()
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trainer = apex_ddpg.ApexDDPGTrainer(
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config=plain_config, env="Pendulum-v1")
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# Test per-worker scale distribution.
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infos = trainer.workers.foreach_policy(
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lambda p, _: p.get_exploration_state())
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scale = [i["cur_scale"] for i in infos]
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expected = [
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0.4**(1 + (i + 1) / float(config["num_workers"] - 1) * 7)
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for i in range(config["num_workers"])
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]
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check(scale, [0.0] + expected)
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for _ 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(trainer)
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# Test again per-worker scale distribution
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# (should not have changed).
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infos = trainer.workers.foreach_policy(
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lambda p, _: p.get_exploration_state())
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scale = [i["cur_scale"] for i in infos]
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check(scale, [0.0] + expected)
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trainer.stop()
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if __name__ == "__main__":
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import sys
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sys.exit(pytest.main(["-v", __file__]))
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