mirror of
https://github.com/vale981/ray
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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.
103 lines
3.8 KiB
Python
103 lines
3.8 KiB
Python
import numpy as np
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import unittest
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import ray.rllib.agents.dqn as dqn
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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 TestDQN(unittest.TestCase):
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def test_dqn_compilation(self):
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"""Test whether a DQNTrainer can be built with both frameworks."""
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config = dqn.DEFAULT_CONFIG.copy()
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config["num_workers"] = 0 # Run locally.
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# tf.
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config["eager"] = False
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trainer = dqn.DQNTrainer(config=config, env="CartPole-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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config["eager"] = True
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trainer = dqn.DQNTrainer(config=config, env="CartPole-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_dqn_exploration_and_soft_q_config(self):
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"""Tests, whether a DQN Agent outputs exploration/softmaxed actions."""
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config = dqn.DEFAULT_CONFIG.copy()
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config["num_workers"] = 0 # Run locally.
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config["env_config"] = {"is_slippery": False, "map_name": "4x4"}
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obs = np.array(0)
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# Test against all frameworks.
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for fw in ["tf", "eager", "torch"]:
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if fw == "torch":
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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 EpsilonGreedy setup.
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trainer = dqn.DQNTrainer(config=config, env="FrozenLake-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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# Low softmax temperature. Behaves like argmax
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# (but no epsilon exploration).
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config["exploration_config"] = {
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"type": "SoftQ",
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"temperature": 0.0
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}
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trainer = dqn.DQNTrainer(config=config, env="FrozenLake-v0")
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# Due to the low temp, always expect the same action.
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a_ = trainer.compute_action(obs)
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for _ in range(50):
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a = trainer.compute_action(obs)
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check(a, a_)
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# Higher softmax temperature.
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config["exploration_config"]["temperature"] = 1.0
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trainer = dqn.DQNTrainer(config=config, env="FrozenLake-v0")
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# Even with the higher temperature, if we set explore=False, we
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# should expect the same actions always.
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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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# Due to the higher temp, expect different actions avg'ing
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# around 1.5.
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actions = []
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for _ in range(300):
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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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# With Random exploration.
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config["exploration_config"] = {"type": "Random"}
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config["explore"] = True
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trainer = dqn.DQNTrainer(config=config, env="FrozenLake-v0")
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actions = []
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for _ in range(300):
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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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if __name__ == "__main__":
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import unittest
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unittest.main(verbosity=1)
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