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https://github.com/vale981/ray
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72 lines
1.9 KiB
Python
72 lines
1.9 KiB
Python
import sys
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import unittest
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import pytest
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import ray
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from ray.rllib.agents import ppo, sac
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from ray.rllib.agents.callbacks import RE3UpdateCallbacks
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class TestRE3(unittest.TestCase):
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"""Tests for RE3 exploration algorithm."""
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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 run_re3(self, rl_algorithm):
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"""Tests RE3 for PPO and SAC.
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Both the on-policy and off-policy setups are validated.
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"""
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if rl_algorithm == "PPO":
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config = ppo.DEFAULT_CONFIG.copy()
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trainer_cls = ppo.PPOTrainer
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beta_schedule = "constant"
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elif rl_algorithm == "SAC":
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config = sac.DEFAULT_CONFIG.copy()
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trainer_cls = sac.SACTrainer
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beta_schedule = "linear_decay"
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class RE3Callbacks(RE3UpdateCallbacks, config["callbacks"]):
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pass
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config["env"] = "Pendulum-v1"
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config["callbacks"] = RE3Callbacks
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config["exploration_config"] = {
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"type": "RE3",
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"embeds_dim": 128,
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"beta_schedule": beta_schedule,
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"sub_exploration": {
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"type": "StochasticSampling",
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},
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}
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num_iterations = 30
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trainer = trainer_cls(config=config)
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learnt = False
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for i in range(num_iterations):
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result = trainer.train()
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print(result)
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if result["episode_reward_max"] > -900.0:
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print("Reached goal after {} iters!".format(i))
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learnt = True
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break
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trainer.stop()
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self.assertTrue(learnt)
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def test_re3_ppo(self):
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"""Tests RE3 with PPO."""
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self.run_re3("PPO")
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def test_re3_sac(self):
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"""Tests RE3 with SAC."""
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self.run_re3("SAC")
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if __name__ == "__main__":
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sys.exit(pytest.main(["-v", __file__]))
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