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https://github.com/wassname/ray.git
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Sven MikaandGitHub
83e06cd30a
* WIP. * WIP. * WIP. * WIP. * WIP. * Fix * WIP. * Add TD3 quick Pendulum regresison. * Cleanup. * Fix. * LINT. * Fix. * Sort quick_learning test cases, add TD3. * Sort quick_learning test cases, add TD3. * Revert test_checkpoint_restore.py (debugging) changes. * Fix old soft_q settings in documentation and test configs. * More doc fixes. * Fix test case. * Fix test case. * Lower test load. * WIP.
88 lines
3.4 KiB
Python
88 lines
3.4 KiB
Python
import numpy as np
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import unittest
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import ray.rllib.agents.ddpg as ddpg
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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
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tf = try_import_tf()
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class TestDDPG(unittest.TestCase):
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def test_ddpg_compilation(self):
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"""Test whether a DDPGTrainer can be built with both frameworks."""
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config = ddpg.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 fw in ["tf", "eager", "torch"]:
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if fw != "tf":
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continue
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config["eager"] = True if fw == "eager" else False
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config["use_pytorch"] = True if fw == "torch" else False
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trainer = ddpg.DDPGTrainer(config=config, env="Pendulum-v0")
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num_iterations = 2
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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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def test_ddpg_exploration_and_with_random_prerun(self):
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"""Tests DDPG's Exploration (w/ random actions for n timesteps)."""
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config = ddpg.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 fw in ["tf", "eager", "torch"]:
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if fw != "tf":
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continue
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config["eager"] = True if fw == "eager" else False
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config["use_pytorch"] = True if fw == "torch" else False
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# Default OUNoise setup.
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trainer = ddpg.DDPGTrainer(config=config, env="Pendulum-v0")
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# Setting explore=False should always return the same action.
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a_ = trainer.compute_action(obs, explore=False)
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for _ in range(50):
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a = trainer.compute_action(obs, explore=False)
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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 _ in range(50):
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actions.append(trainer.compute_action(obs))
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check(np.std(actions), 0.0, false=True)
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# Check randomness at beginning.
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config["exploration_config"] = {
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# Act randomly at beginning ...
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"random_timesteps": 50,
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# Then act very closely to deterministic actions thereafter.
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"ou_base_scale": 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 = ddpg.DDPGTrainer(config=config, env="Pendulum-v0")
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# ts=1 (get a deterministic action as per explore=False).
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deterministic_action = trainer.compute_action(obs, explore=False)
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# ts=2-5 (in random window).
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random_a = []
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for _ in range(49):
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random_a.append(trainer.compute_action(obs, explore=True))
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check(random_a[-1], deterministic_action, false=True)
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self.assertTrue(np.std(random_a) > 0.5)
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# ts > 50 (a=deterministic_action + scale * N[0,1])
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for _ in range(50):
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a = trainer.compute_action(obs, explore=True)
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check(a, deterministic_action, rtol=0.1)
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# ts >> 50 (BUT: explore=False -> expect deterministic action).
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for _ in range(50):
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a = trainer.compute_action(obs, explore=False)
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check(a, deterministic_action)
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if __name__ == "__main__":
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import unittest
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unittest.main(verbosity=1)
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