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Lint Python files with Yapf (#1872)

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Philipp Moritz
2018-04-11 10:11:35 -07:00
committed by Robert Nishihara
parent a3ddde398c
commit 74162d1492
97 changed files with 3927 additions and 3139 deletions
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python/ray/tune/examples/pbt_ppo_example.py
+36 -22
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@@ -1,5 +1,4 @@
#!/usr/bin/env python
"""Example of using PBT with RLlib.
Note that this requires a cluster with at least 8 GPUs in order for all trials
@@ -30,7 +29,8 @@ if __name__ == "__main__":
return config
pbt = PopulationBasedTraining(
time_attr="time_total_s", reward_attr="episode_reward_mean",
time_attr="time_total_s",
reward_attr="episode_reward_mean",
perturbation_interval=120,
resample_probability=0.25,
# Specifies the mutations of these hyperparams
@@ -45,26 +45,40 @@ if __name__ == "__main__":
custom_explore_fn=explore)
ray.init()
run_experiments({
"pbt_humanoid_test": {
"run": "PPO",
"env": "Humanoid-v1",
"repeat": 8,
"trial_resources": {"cpu": 4, "gpu": 1},
"config": {
"kl_coeff": 1.0,
"num_workers": 8,
"devices": ["/gpu:0"],
"model": {"free_log_std": True},
# These params are tuned from a fixed starting value.
"lambda": 0.95,
"clip_param": 0.2,
"sgd_stepsize": 1e-4,
# These params start off randomly drawn from a set.
"num_sgd_iter": lambda spec: random.choice([10, 20, 30]),
"sgd_batchsize": lambda spec: random.choice([128, 512, 2048]),
"timesteps_per_batch":
run_experiments(
{
"pbt_humanoid_test": {
"run": "PPO",
"env": "Humanoid-v1",
"repeat": 8,
"trial_resources": {
"cpu": 4,
"gpu": 1
},
"config": {
"kl_coeff":
1.0,
"num_workers":
8,
"devices": ["/gpu:0"],
"model": {
"free_log_std": True
},
# These params are tuned from a fixed starting value.
"lambda":
0.95,
"clip_param":
0.2,
"sgd_stepsize":
1e-4,
# These params start off randomly drawn from a set.
"num_sgd_iter":
lambda spec: random.choice([10, 20, 30]),
"sgd_batchsize":
lambda spec: random.choice([128, 512, 2048]),
"timesteps_per_batch":
lambda spec: random.choice([10000, 20000, 40000])
},
},
},
}, scheduler=pbt)
scheduler=pbt)