ray/rllib/agents/ppo/tests/test_ddppo.py
Sven Mika 22ccc43670
[RLlib] DQN torch version. (#7597)
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* Update rllib/evaluation/sampler.py

Co-Authored-By: Eric Liang <ekhliang@gmail.com>

* Update rllib/evaluation/sampler.py

Co-Authored-By: Eric Liang <ekhliang@gmail.com>

* Update rllib/utils/exploration/exploration.py

Co-Authored-By: Eric Liang <ekhliang@gmail.com>

* Update rllib/utils/exploration/exploration.py

Co-Authored-By: Eric Liang <ekhliang@gmail.com>

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* Update rllib/utils/exploration/exploration.py

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Co-Authored-By: Eric Liang <ekhliang@gmail.com>

* Update rllib/policy/dynamic_tf_policy.py

Co-Authored-By: Eric Liang <ekhliang@gmail.com>

* Update rllib/policy/dynamic_tf_policy.py

Co-Authored-By: Eric Liang <ekhliang@gmail.com>

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* Working SimpleQ learning cartpole on both torch AND tf.

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* Update docs and add torch to APEX test.

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* Fix broken RLlib tests in master.

* Split BAZEL learning tests into cartpole and pendulum (reached the 60min barrier).

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Co-authored-by: Eric Liang <ekhliang@gmail.com>
2020-04-06 11:56:16 -07:00

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Python

import unittest
import ray
import ray.rllib.agents.ppo as ppo
from ray.rllib.utils.framework import try_import_tf
from ray.rllib.utils.test_utils import framework_iterator
tf = try_import_tf()
class TestDDPPO(unittest.TestCase):
@classmethod
def setUpClass(cls):
ray.init()
@classmethod
def tearDownClass(cls):
ray.shutdown()
def test_ddppo_compilation(self):
"""Test whether a DDPPOTrainer can be built with both frameworks."""
config = ppo.ddppo.DEFAULT_CONFIG.copy()
config["num_gpus_per_worker"] = 0
num_iterations = 2
for _ in framework_iterator(config, "torch"):
trainer = ppo.ddppo.DDPPOTrainer(config=config, env="CartPole-v0")
for i in range(num_iterations):
trainer.train()
if __name__ == "__main__":
import pytest
import sys
sys.exit(pytest.main(["-v", __file__]))