mirror of
https://github.com/wassname/ray.git
synced 2026-07-06 05:16:30 +08:00
Eric Liang
60 lines
1.5 KiB
Python
Executable File
60 lines
1.5 KiB
Python
Executable File
#!/usr/bin/env python
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import numpy as np
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import ray
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from ray.rllib.agent import get_agent_class
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def get_mean_action(alg, obs):
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out = []
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for _ in range(2000):
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out.append(float(alg.compute_action(obs)))
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return np.mean(out)
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ray.init()
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CONFIGS = {
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"ES": {"episodes_per_batch": 10, "timesteps_per_batch": 100},
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"DQN": {},
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"PPO": {"num_sgd_iter": 5, "timesteps_per_batch": 1000},
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"A3C": {"use_lstm": False},
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}
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def test(use_object_store, alg_name):
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cls = get_agent_class(alg_name)
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alg1 = cls(config=CONFIGS[name], env="CartPole-v0")
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alg2 = cls(config=CONFIGS[name], env="CartPole-v0")
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for _ in range(3):
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res = alg1.train()
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print("current status: " + str(res))
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# Sync the models
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if use_object_store:
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alg2.restore_from_object(alg1.save_to_object())
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else:
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alg2.restore(alg1.save())
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for _ in range(10):
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obs = np.random.uniform(size=4)
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a1 = get_mean_action(alg1, obs)
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a2 = get_mean_action(alg2, obs)
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print("Checking computed actions", alg1, obs, a1, a2)
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assert abs(a1 - a2) < .1, (a1, a2)
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if __name__ == "__main__":
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# https://github.com/ray-project/ray/issues/1062 for enabling ES test too
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for use_object_store in [False, True]:
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for name in ["ES", "DQN", "PPO", "A3C"]:
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test(use_object_store, name)
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print("All checkpoint restore tests passed!")
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