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[rllib] format with yapf (#2427)

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* initial yapf

* manual fix yapf bugs
This commit is contained in:
Eric Liang
2018-07-19 15:30:36 -07:00
committed by GitHub
parent 24eb140e07
commit d01dc9e22d
86 changed files with 1276 additions and 978 deletions
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python/ray/rllib/agents/ppo/ppo.py
+16 -15
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@@ -77,28 +77,30 @@ class PPOAgent(Agent):
self.local_evaluator = self.make_local_evaluator(
self.env_creator, PPOPolicyGraph)
self.remote_evaluators = self.make_remote_evaluators(
self.env_creator, PPOPolicyGraph, self.config["num_workers"],
{"num_cpus": self.config["num_cpus_per_worker"],
"num_gpus": self.config["num_gpus_per_worker"]})
self.env_creator, PPOPolicyGraph, self.config["num_workers"], {
"num_cpus": self.config["num_cpus_per_worker"],
"num_gpus": self.config["num_gpus_per_worker"]
})
if self.config["simple_optimizer"]:
self.optimizer = SyncSamplesOptimizer(
self.local_evaluator, self.remote_evaluators,
{"num_sgd_iter": self.config["num_sgd_iter"]})
else:
self.optimizer = LocalMultiGPUOptimizer(
self.local_evaluator, self.remote_evaluators,
{"sgd_batch_size": self.config["sgd_batchsize"],
"sgd_stepsize": self.config["sgd_stepsize"],
"num_sgd_iter": self.config["num_sgd_iter"],
"timesteps_per_batch": self.config["timesteps_per_batch"],
"standardize_fields": ["advantages"]})
self.local_evaluator, self.remote_evaluators, {
"sgd_batch_size": self.config["sgd_batchsize"],
"sgd_stepsize": self.config["sgd_stepsize"],
"num_sgd_iter": self.config["num_sgd_iter"],
"timesteps_per_batch": self.config["timesteps_per_batch"],
"standardize_fields": ["advantages"]
})
def _train(self):
prev_steps = self.optimizer.num_steps_sampled
fetches = self.optimizer.step()
self.local_evaluator.for_policy(lambda pi: pi.update_kl(fetches["kl"]))
FilterManager.synchronize(
self.local_evaluator.filters, self.remote_evaluators)
FilterManager.synchronize(self.local_evaluator.filters,
self.remote_evaluators)
res = self.optimizer.collect_metrics()
res = res._replace(
timesteps_this_iter=self.optimizer.num_steps_sampled - prev_steps,
@@ -115,9 +117,7 @@ class PPOAgent(Agent):
"checkpoint-{}".format(self.iteration))
agent_state = ray.get(
[a.save.remote() for a in self.remote_evaluators])
extra_data = [
self.local_evaluator.save(),
agent_state]
extra_data = [self.local_evaluator.save(), agent_state]
pickle.dump(extra_data, open(checkpoint_path + ".extra_data", "wb"))
return checkpoint_path
@@ -126,4 +126,5 @@ class PPOAgent(Agent):
self.local_evaluator.restore(extra_data[0])
ray.get([
a.restore.remote(o)
for (a, o) in zip(self.remote_evaluators, extra_data[1])])
for (a, o) in zip(self.remote_evaluators, extra_data[1])
])