mirror of
https://github.com/vale981/ray
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103 lines
4.1 KiB
Python
103 lines
4.1 KiB
Python
import numpy as np
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import unittest
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import ray
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import ray.rllib.agents.ddpg.td3 as td3
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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, 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 TestTD3(unittest.TestCase):
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@classmethod
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def setUpClass(cls) -> None:
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ray.init()
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@classmethod
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def tearDownClass(cls) -> None:
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ray.shutdown()
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def test_td3_compilation(self):
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"""Test whether a TD3Trainer can be built with both frameworks."""
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config = td3.TD3_DEFAULT_CONFIG.copy()
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config["num_workers"] = 0 # Run locally.
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# Test against all frameworks.
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for _ in framework_iterator(config):
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trainer = td3.TD3Trainer(config=config, env="Pendulum-v0")
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num_iterations = 1
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for i in range(num_iterations):
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results = trainer.train()
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print(results)
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check_compute_single_action(trainer)
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trainer.stop()
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def test_td3_exploration_and_with_random_prerun(self):
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"""Tests TD3's Exploration (w/ random actions for n timesteps)."""
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config = td3.TD3_DEFAULT_CONFIG.copy()
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config["num_workers"] = 0 # Run locally.
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obs = np.array([0.0, 0.1, -0.1])
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# Test against all frameworks.
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for _ in framework_iterator(config):
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lcl_config = config.copy()
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# Default GaussianNoise setup.
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trainer = td3.TD3Trainer(config=lcl_config, env="Pendulum-v0")
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# Setting explore=False should always return the same action.
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a_ = trainer.compute_single_action(obs, explore=False)
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self.assertEqual(trainer.get_policy().global_timestep, 1)
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for i in range(50):
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a = trainer.compute_single_action(obs, explore=False)
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self.assertEqual(trainer.get_policy().global_timestep, i + 2)
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check(a, a_)
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# explore=None (default: explore) should return different actions.
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actions = []
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for i in range(50):
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actions.append(trainer.compute_single_action(obs))
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self.assertEqual(trainer.get_policy().global_timestep, i + 52)
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check(np.std(actions), 0.0, false=True)
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trainer.stop()
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# Check randomness at beginning.
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lcl_config["exploration_config"] = {
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# Act randomly at beginning ...
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"random_timesteps": 30,
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# Then act very closely to deterministic actions thereafter.
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"stddev": 0.001,
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"initial_scale": 0.001,
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"final_scale": 0.001,
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}
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trainer = td3.TD3Trainer(config=lcl_config, env="Pendulum-v0")
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# ts=0 (get a deterministic action as per explore=False).
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deterministic_action = trainer.compute_single_action(
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obs, explore=False)
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self.assertEqual(trainer.get_policy().global_timestep, 1)
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# ts=1-29 (in random window).
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random_a = []
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for i in range(1, 30):
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random_a.append(
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trainer.compute_single_action(obs, explore=True))
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self.assertEqual(trainer.get_policy().global_timestep, i + 1)
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check(random_a[-1], deterministic_action, false=True)
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self.assertTrue(np.std(random_a) > 0.3)
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# ts > 30 (a=deterministic_action + scale * N[0,1])
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for i in range(50):
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a = trainer.compute_single_action(obs, explore=True)
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self.assertEqual(trainer.get_policy().global_timestep, i + 31)
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check(a, deterministic_action, rtol=0.1)
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# ts >> 30 (BUT: explore=False -> expect deterministic action).
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for i in range(50):
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a = trainer.compute_single_action(obs, explore=False)
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self.assertEqual(trainer.get_policy().global_timestep, i + 81)
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check(a, deterministic_action)
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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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