mirror of
https://github.com/vale981/ray
synced 2025-03-10 21:36:39 -04:00
499ad5fbe4
- Translate all vtrace functionality to torch and added torch to the framework_iterator-loop in all existing vtrace test cases. - Add learning test cases for APPO torch (both w/ and w/o v-trace). - Add quick compilation tests for APPO (tf and torch, v-trace and no v-trace).
42 lines
1.2 KiB
Python
42 lines
1.2 KiB
Python
import unittest
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import ray
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import ray.rllib.agents.ppo as ppo
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from ray.rllib.utils.framework import try_import_tf
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from ray.rllib.utils.test_utils import framework_iterator
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tf = try_import_tf()
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class TestAPPO(unittest.TestCase):
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@classmethod
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def setUpClass(cls):
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ray.init()
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@classmethod
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def tearDownClass(cls):
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ray.shutdown()
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def test_appo_compilation(self):
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"""Test whether an APPOTrainer can be built with both frameworks."""
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config = ppo.appo.DEFAULT_CONFIG.copy()
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config["num_workers"] = 1
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num_iterations = 2
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for _ in framework_iterator(config, frameworks=("torch", "tf")):
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_config = config.copy()
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trainer = ppo.APPOTrainer(config=_config, env="CartPole-v0")
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for i in range(num_iterations):
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print(trainer.train())
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_config = config.copy()
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_config["vtrace"] = True
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trainer = ppo.APPOTrainer(config=_config, env="CartPole-v0")
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for i in range(num_iterations):
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print(trainer.train())
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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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