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[tune] Allow perturbations of categorical variables (#1507)

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* categorical perturb

* Sat Feb  3 00:28:13 PST 2018

* explicitly clean up nested actors

* Sat Feb 10 02:21:57 PST 2018

* Sat Feb 10 02:22:29 PST 2018
This commit is contained in:
Eric Liang
2018-02-11 18:20:27 -08:00
committed by GitHub
parent 639df85fda
commit b6a06b81ed
4 changed files with 78 additions and 26 deletions
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python/ray/tune/examples/pbt_ppo_example.py
+10 -11
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@@ -33,15 +33,14 @@ if __name__ == "__main__":
time_attr="time_total_s", reward_attr="episode_reward_mean",
perturbation_interval=120,
resample_probability=0.25,
# Specifies the resampling distributions of these hyperparams
# Specifies the mutations of these hyperparams
hyperparam_mutations={
"lambda": lambda config: random.uniform(0.9, 1.0),
"clip_param": lambda config: random.uniform(0.01, 0.5),
"sgd_stepsize": lambda config: random.uniform(.00001, .001),
"num_sgd_iter": lambda config: random.randint(1, 30),
"sgd_batchsize": lambda config: random.randint(128, 16384),
"timesteps_per_batch":
lambda config: random.randint(2000, 160000),
"lambda": lambda: random.uniform(0.9, 1.0),
"clip_param": lambda: random.uniform(0.01, 0.5),
"sgd_stepsize": [1e-3, 5e-4, 1e-4, 5e-5, 1e-5],
"num_sgd_iter": lambda: random.randint(1, 30),
"sgd_batchsize": lambda: random.randint(128, 16384),
"timesteps_per_batch": lambda: random.randint(2000, 160000),
},
custom_explore_fn=explore)
@@ -57,11 +56,11 @@ if __name__ == "__main__":
"num_workers": 8,
"devices": ["/gpu:0"],
"model": {"free_log_std": True},
# These params are tuned from their starting value
# These params are tuned from a fixed starting value.
"lambda": 0.95,
"clip_param": 0.2,
# Start off with several random variations
"sgd_stepsize": lambda spec: random.uniform(.00001, .001),
"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":