mirror of
https://github.com/wassname/ray.git
synced 2026-07-13 17:45:08 +08:00
Robert Nishihara
e0867c8845
* 4 space indentation for actor.py. * 4 space indentation for worker.py. * 4 space indentation for more files. * 4 space indentation for some test files. * Check indentation in Travis. * 4 space indentation for some rl files. * Fix failure test. * Fix multi_node_test. * 4 space indentation for more files. * 4 space indentation for remaining files. * Fixes.
213 lines
8.3 KiB
Python
213 lines
8.3 KiB
Python
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 os
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import time
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import numpy as np
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import tensorflow as tf
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import ray
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from ray.rllib.common import Algorithm, TrainingResult
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from ray.rllib.policy_gradient.agent import Agent, RemoteAgent
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from ray.rllib.policy_gradient.env import (
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NoPreprocessor, AtariRamPreprocessor, AtariPixelPreprocessor)
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from ray.rllib.policy_gradient.rollout import collect_samples
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from ray.rllib.policy_gradient.utils import shuffle
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DEFAULT_CONFIG = {
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"kl_coeff": 0.2,
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"num_sgd_iter": 30,
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"max_iterations": 1000,
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"sgd_stepsize": 5e-5,
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# TODO(pcm): Expose the choice between gpus and cpus
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# as a command line argument.
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"devices": ["/cpu:%d" % i for i in range(4)],
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"tf_session_args": {
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"device_count": {"CPU": 4},
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"log_device_placement": False,
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"allow_soft_placement": True,
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},
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"sgd_batchsize": 128, # total size across all devices
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"entropy_coeff": 0.0,
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"clip_param": 0.3,
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"kl_target": 0.01,
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"timesteps_per_batch": 40000,
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"num_agents": 5,
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"full_trace_nth_sgd_batch": -1,
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"full_trace_data_load": False,
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"use_tf_debugger": False,
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"model_checkpoint_file": "iteration-%s.ckpt"}
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class PolicyGradient(Algorithm):
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def __init__(self, env_name, config, upload_dir=None):
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config.update({"alg": "PolicyGradient"})
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Algorithm.__init__(self, env_name, config, upload_dir=upload_dir)
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# TODO(ekl): The preprocessor should be associated with the env
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# elsewhere.
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if self.env_name == "Pong-v0":
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preprocessor = AtariPixelPreprocessor()
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elif self.env_name == "Pong-ram-v3":
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preprocessor = AtariRamPreprocessor()
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elif self.env_name == "CartPole-v0":
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preprocessor = NoPreprocessor()
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elif self.env_name == "Walker2d-v1":
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preprocessor = NoPreprocessor()
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else:
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preprocessor = AtariPixelPreprocessor()
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self.preprocessor = preprocessor
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self.global_step = 0
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self.j = 0
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self.kl_coeff = config["kl_coeff"]
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self.model = Agent(
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self.env_name, 1, self.preprocessor, self.config, self.logdir,
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False)
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self.agents = [
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RemoteAgent.remote(
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self.env_name, 1, self.preprocessor, self.config,
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self.logdir, True)
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for _ in range(config["num_agents"])]
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def train(self):
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agents = self.agents
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config = self.config
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model = self.model
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j = self.j
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self.j += 1
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saver = tf.train.Saver(max_to_keep=None)
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if "load_checkpoint" in config:
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saver.restore(model.sess, config["load_checkpoint"])
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# TF does not support to write logs to S3 at the moment
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write_tf_logs = self.logdir.startswith("file")
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iter_start = time.time()
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if write_tf_logs:
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file_writer = tf.summary.FileWriter(self.logdir, model.sess.graph)
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if config["model_checkpoint_file"]:
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checkpoint_path = saver.save(
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model.sess,
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os.path.join(self.logdir,
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config["model_checkpoint_file"] % j))
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print("Checkpoint saved in file: %s" % checkpoint_path)
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checkpointing_end = time.time()
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weights = ray.put(model.get_weights())
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[a.load_weights.remote(weights) for a in agents]
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trajectory, total_reward, traj_len_mean = collect_samples(
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agents, config["timesteps_per_batch"], 0.995, 1.0, 2000)
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print("total reward is ", total_reward)
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print("trajectory length mean is ", traj_len_mean)
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print("timesteps:", trajectory["dones"].shape[0])
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if write_tf_logs:
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traj_stats = tf.Summary(value=[
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tf.Summary.Value(
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tag="policy_gradient/rollouts/mean_reward",
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simple_value=total_reward),
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tf.Summary.Value(
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tag="policy_gradient/rollouts/traj_len_mean",
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simple_value=traj_len_mean)])
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file_writer.add_summary(traj_stats, self.global_step)
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self.global_step += 1
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trajectory["advantages"] = ((trajectory["advantages"] -
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trajectory["advantages"].mean()) /
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trajectory["advantages"].std())
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rollouts_end = time.time()
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print("Computing policy (iterations=" + str(config["num_sgd_iter"]) +
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", stepsize=" + str(config["sgd_stepsize"]) + "):")
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names = ["iter", "loss", "kl", "entropy"]
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print(("{:>15}" * len(names)).format(*names))
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trajectory = shuffle(trajectory)
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shuffle_end = time.time()
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tuples_per_device = model.load_data(
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trajectory, j == 0 and config["full_trace_data_load"])
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load_end = time.time()
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checkpointing_time = checkpointing_end - iter_start
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rollouts_time = rollouts_end - checkpointing_end
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shuffle_time = shuffle_end - rollouts_end
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load_time = load_end - shuffle_end
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sgd_time = 0
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for i in range(config["num_sgd_iter"]):
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sgd_start = time.time()
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batch_index = 0
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num_batches = (int(tuples_per_device) //
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int(model.per_device_batch_size))
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loss, kl, entropy = [], [], []
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permutation = np.random.permutation(num_batches)
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while batch_index < num_batches:
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full_trace = (
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i == 0 and j == 0 and
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batch_index == config["full_trace_nth_sgd_batch"])
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batch_loss, batch_kl, batch_entropy = model.run_sgd_minibatch(
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permutation[batch_index] * model.per_device_batch_size,
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self.kl_coeff, full_trace,
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file_writer if write_tf_logs else None)
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loss.append(batch_loss)
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kl.append(batch_kl)
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entropy.append(batch_entropy)
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batch_index += 1
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loss = np.mean(loss)
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kl = np.mean(kl)
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entropy = np.mean(entropy)
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sgd_end = time.time()
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print("{:>15}{:15.5e}{:15.5e}{:15.5e}".format(i, loss, kl,
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entropy))
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values = []
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if i == config["num_sgd_iter"] - 1:
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metric_prefix = "policy_gradient/sgd/final_iter/"
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values.append(tf.Summary.Value(
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tag=metric_prefix + "kl_coeff",
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simple_value=self.kl_coeff))
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else:
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metric_prefix = "policy_gradient/sgd/intermediate_iters/"
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values.extend([
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tf.Summary.Value(
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tag=metric_prefix + "mean_entropy",
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simple_value=entropy),
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tf.Summary.Value(
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tag=metric_prefix + "mean_loss",
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simple_value=loss),
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tf.Summary.Value(
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tag=metric_prefix + "mean_kl",
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simple_value=kl)])
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if write_tf_logs:
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sgd_stats = tf.Summary(value=values)
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file_writer.add_summary(sgd_stats, self.global_step)
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self.global_step += 1
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sgd_time += sgd_end - sgd_start
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if kl > 2.0 * config["kl_target"]:
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self.kl_coeff *= 1.5
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elif kl < 0.5 * config["kl_target"]:
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self.kl_coeff *= 0.5
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info = {
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"kl_divergence": kl,
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"kl_coefficient": self.kl_coeff,
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"checkpointing_time": checkpointing_time,
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"rollouts_time": rollouts_time,
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"shuffle_time": shuffle_time,
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"load_time": load_time,
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"sgd_time": sgd_time,
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"sample_throughput": len(trajectory["observations"]) / sgd_time
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}
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print("kl div:", kl)
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print("kl coeff:", self.kl_coeff)
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print("checkpointing time:", checkpointing_time)
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print("rollouts time:", rollouts_time)
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print("shuffle time:", shuffle_time)
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print("load time:", load_time)
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print("sgd time:", sgd_time)
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print("sgd examples/s:", len(trajectory["observations"]) / sgd_time)
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result = TrainingResult(
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self.experiment_id.hex, j, total_reward, traj_len_mean, info)
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return result
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