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https://github.com/vale981/ray
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58 lines
2 KiB
Python
58 lines
2 KiB
Python
"""Integration test: (1) pendulum works, (2) single-agent multi-agent works."""
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import unittest
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import ray
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from ray.tune import run_experiments
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from ray.tune.registry import register_env
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from ray.rllib.examples.env.multi_agent import MultiAgentPendulum
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from ray.rllib.utils.test_utils import framework_iterator
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class TestMultiAgentPendulum(unittest.TestCase):
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def setUp(self) -> None:
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ray.init()
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def tearDown(self) -> None:
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ray.shutdown()
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def test_multi_agent_pendulum(self):
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register_env("multi_agent_pendulum",
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lambda _: MultiAgentPendulum({"num_agents": 1}))
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# Test for both torch and tf.
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for fw in framework_iterator(frameworks=["torch", "tf"]):
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trials = run_experiments({
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"test": {
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"run": "PPO",
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"env": "multi_agent_pendulum",
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"stop": {
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"timesteps_total": 500000,
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"episode_reward_mean": -300.0,
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},
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"config": {
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"train_batch_size": 2048,
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"vf_clip_param": 10.0,
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"num_workers": 0,
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"num_envs_per_worker": 10,
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"lambda": 0.1,
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"gamma": 0.95,
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"lr": 0.0003,
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"sgd_minibatch_size": 64,
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"num_sgd_iter": 10,
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"model": {
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"fcnet_hiddens": [128, 128],
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},
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"batch_mode": "complete_episodes",
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"framework": fw,
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},
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}
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}, verbose=1)
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if trials[0].last_result["episode_reward_mean"] < -300.0:
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raise ValueError("Did not get to -200 reward",
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trials[0].last_result)
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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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