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71 lines
1.9 KiB
Python
71 lines
1.9 KiB
Python
#!/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(num_cpus=10)
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CONFIGS = {
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"ES": {"episodes_per_batch": 10, "timesteps_per_batch": 100,
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"num_workers": 2},
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"DQN": {},
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"DDPG": {"noise_scale": 0.0, "timesteps_per_iteration": 100},
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"PPO": {"num_sgd_iter": 5, "timesteps_per_batch": 1000, "num_workers": 2},
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"A3C": {"use_lstm": False, "num_workers": 1},
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}
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def test(use_object_store, alg_name, failures):
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cls = get_agent_class(alg_name)
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if alg_name == "DDPG":
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alg1 = cls(config=CONFIGS[name], env="Pendulum-v0")
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alg2 = cls(config=CONFIGS[name], env="Pendulum-v0")
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else:
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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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if alg_name == "DDPG":
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obs = np.random.uniform(size=3)
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else:
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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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if abs(a1 - a2) > .1:
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failures.append((alg_name, [a1, a2]))
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if __name__ == "__main__":
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failures = []
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for use_object_store in [False, True]:
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for name in ["ES", "DQN", "DDPG", "PPO", "A3C"]:
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test(use_object_store, name, failures)
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assert not failures, failures
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print("All checkpoint restore tests passed!")
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