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https://github.com/vale981/ray
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63 lines
2 KiB
Python
63 lines
2 KiB
Python
import os
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from pathlib import Path
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import unittest
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import ray
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import ray.rllib.agents.marwil as marwil
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from ray.rllib.utils.framework import try_import_tf
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from ray.rllib.utils.test_utils import check_compute_single_action, \
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framework_iterator
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tf1, tf, tfv = try_import_tf()
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class TestMARWIL(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_marwil_compilation_and_learning_from_offline_file(self):
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"""Test whether a MARWILTrainer can be built with all frameworks.
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And learns from a historic-data file.
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"""
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rllib_dir = Path(__file__).parent.parent.parent.parent
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print("rllib dir={}".format(rllib_dir))
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data_file = os.path.join(rllib_dir, "tests/data/cartpole/large.json")
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print("data_file={} exists={}".format(data_file,
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os.path.isfile(data_file)))
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config = marwil.DEFAULT_CONFIG.copy()
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config["num_workers"] = 0 # Run locally.
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config["evaluation_num_workers"] = 1
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config["evaluation_interval"] = 1
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config["evaluation_config"] = {"input": "sampler"}
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config["input"] = [data_file]
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num_iterations = 300
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# Test for all frameworks.
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for _ in framework_iterator(config):
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trainer = marwil.MARWILTrainer(config=config, env="CartPole-v0")
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for i in range(num_iterations):
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eval_results = trainer.train()["evaluation"]
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print("iter={} R={}".format(
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i, eval_results["episode_reward_mean"]))
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# Learn until some reward is reached on an actual live env.
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if eval_results["episode_reward_mean"] > 60.0:
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print("learnt!")
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break
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check_compute_single_action(
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trainer, include_prev_action_reward=True)
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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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