mirror of
https://github.com/wassname/ray.git
synced 2026-06-29 05:34:49 +08:00
Philipp Moritz
9bcaaaeaf5
* configuration option for tensorflow debugger * add model checkpointing * fix linting * make it possible to run without checkpointing * fix * loading from checkpoint and expose debugger through cli * todo for filters * Fix typo.
195 lines
7.2 KiB
Python
195 lines
7.2 KiB
Python
from __future__ import absolute_import
|
|
from __future__ import division
|
|
from __future__ import print_function
|
|
|
|
from datetime import datetime
|
|
|
|
import argparse
|
|
import time
|
|
|
|
import ray
|
|
import numpy as np
|
|
import tensorflow as tf
|
|
|
|
from reinforce.env import (NoPreprocessor, AtariRamPreprocessor,
|
|
AtariPixelPreprocessor)
|
|
from reinforce.agent import Agent, RemoteAgent
|
|
from reinforce.rollout import collect_samples
|
|
from reinforce.utils import shuffle
|
|
|
|
|
|
config = {"kl_coeff": 0.2,
|
|
"num_sgd_iter": 30,
|
|
"max_iterations": 1000,
|
|
"sgd_stepsize": 5e-5,
|
|
# TODO(pcm): Expose the choice between gpus and cpus
|
|
# as a command line argument.
|
|
"devices": ["/cpu:%d" % i for i in range(4)],
|
|
"tf_session_args": {
|
|
"device_count": {"CPU": 4},
|
|
"log_device_placement": False,
|
|
"allow_soft_placement": True,
|
|
},
|
|
"sgd_batchsize": 128, # total size across all devices
|
|
"entropy_coeff": 0.0,
|
|
"clip_param": 0.3,
|
|
"kl_target": 0.01,
|
|
"timesteps_per_batch": 40000,
|
|
"num_agents": 5,
|
|
"tensorboard_log_dir": "/tmp/ray",
|
|
"full_trace_nth_sgd_batch": -1,
|
|
"full_trace_data_load": False,
|
|
"model_checkpoint_file": "/tmp/iteration-%s.ckpt"}
|
|
|
|
|
|
if __name__ == "__main__":
|
|
parser = argparse.ArgumentParser(description="Run the policy gradient "
|
|
"algorithm.")
|
|
parser.add_argument("--environment", default="Pong-v0", type=str,
|
|
help="The gym environment to use.")
|
|
parser.add_argument("--redis-address", default=None, type=str,
|
|
help="The Redis address of the cluster.")
|
|
parser.add_argument("--use-tf-debugger", default=False, type=bool,
|
|
help="Run the script inside of tf-dbg.")
|
|
parser.add_argument("--load-checkpoint", default=None, type=str,
|
|
help="Continue training from a checkpoint.")
|
|
|
|
args = parser.parse_args()
|
|
config["use_tf_debugger"] = args.use_tf_debugger
|
|
|
|
ray.init(redis_address=args.redis_address)
|
|
|
|
mdp_name = args.environment
|
|
if args.environment == "Pong-v0":
|
|
preprocessor = AtariPixelPreprocessor()
|
|
elif mdp_name == "Pong-ram-v3":
|
|
preprocessor = AtariRamPreprocessor()
|
|
elif mdp_name == "CartPole-v0":
|
|
preprocessor = NoPreprocessor()
|
|
elif mdp_name == "Walker2d-v1":
|
|
preprocessor = NoPreprocessor()
|
|
else:
|
|
print("No environment was chosen, so defaulting to Pong-v0.")
|
|
mdp_name = "Pong-v0"
|
|
preprocessor = AtariPixelPreprocessor()
|
|
|
|
print("Using the environment {}.".format(mdp_name))
|
|
agents = [RemoteAgent.remote(mdp_name, 1, preprocessor, config, True)
|
|
for _ in range(config["num_agents"])]
|
|
agent = Agent(mdp_name, 1, preprocessor, config, False)
|
|
|
|
kl_coeff = config["kl_coeff"]
|
|
|
|
file_writer = tf.summary.FileWriter(
|
|
"{}/trpo_{}_{}".format(
|
|
config["tensorboard_log_dir"], mdp_name,
|
|
str(datetime.today()).replace(" ", "_")),
|
|
agent.sess.graph)
|
|
|
|
global_step = 0
|
|
|
|
saver = tf.train.Saver(max_to_keep=None)
|
|
if args.load_checkpoint:
|
|
saver.restore(agent.sess, args.load_checkpoint)
|
|
|
|
for j in range(config["max_iterations"]):
|
|
iter_start = time.time()
|
|
print("\n== iteration", j)
|
|
if config["model_checkpoint_file"]:
|
|
checkpoint_path = saver.save(
|
|
agent.sess, config["model_checkpoint_file"] % j)
|
|
print("Checkpoint saved in file: %s" % checkpoint_path)
|
|
checkpointing_end = time.time()
|
|
weights = ray.put(agent.get_weights())
|
|
[a.load_weights.remote(weights) for a in agents]
|
|
trajectory, total_reward, traj_len_mean = collect_samples(
|
|
agents, config["timesteps_per_batch"], 0.995, 1.0, 2000)
|
|
print("total reward is ", total_reward)
|
|
print("trajectory length mean is ", traj_len_mean)
|
|
print("timesteps:", trajectory["dones"].shape[0])
|
|
traj_stats = tf.Summary(value=[
|
|
tf.Summary.Value(
|
|
tag="policy_gradient/rollouts/mean_reward",
|
|
simple_value=total_reward),
|
|
tf.Summary.Value(
|
|
tag="policy_gradient/rollouts/traj_len_mean",
|
|
simple_value=traj_len_mean)])
|
|
file_writer.add_summary(traj_stats, global_step)
|
|
global_step += 1
|
|
trajectory["advantages"] = ((trajectory["advantages"] -
|
|
trajectory["advantages"].mean()) /
|
|
trajectory["advantages"].std())
|
|
rollouts_end = time.time()
|
|
print("Computing policy (iterations=" + str(config["num_sgd_iter"]) +
|
|
", stepsize=" + str(config["sgd_stepsize"]) + "):")
|
|
names = ["iter", "loss", "kl", "entropy"]
|
|
print(("{:>15}" * len(names)).format(*names))
|
|
num_devices = len(config["devices"])
|
|
trajectory = shuffle(trajectory)
|
|
shuffle_end = time.time()
|
|
tuples_per_device = agent.load_data(
|
|
trajectory, j == 0 and config["full_trace_data_load"])
|
|
load_end = time.time()
|
|
checkpointing_time = checkpointing_end - iter_start
|
|
rollouts_time = rollouts_end - checkpointing_end
|
|
shuffle_time = shuffle_end - rollouts_end
|
|
load_time = load_end - shuffle_end
|
|
sgd_time = 0
|
|
for i in range(config["num_sgd_iter"]):
|
|
sgd_start = time.time()
|
|
batch_index = 0
|
|
num_batches = int(tuples_per_device) // int(agent.per_device_batch_size)
|
|
loss, kl, entropy = [], [], []
|
|
permutation = np.random.permutation(num_batches)
|
|
while batch_index < num_batches:
|
|
full_trace = (
|
|
i == 0 and j == 0 and
|
|
batch_index == config["full_trace_nth_sgd_batch"])
|
|
batch_loss, batch_kl, batch_entropy = agent.run_sgd_minibatch(
|
|
permutation[batch_index] * agent.per_device_batch_size,
|
|
kl_coeff, full_trace, file_writer)
|
|
loss.append(batch_loss)
|
|
kl.append(batch_kl)
|
|
entropy.append(batch_entropy)
|
|
batch_index += 1
|
|
loss = np.mean(loss)
|
|
kl = np.mean(kl)
|
|
entropy = np.mean(entropy)
|
|
sgd_end = time.time()
|
|
print("{:>15}{:15.5e}{:15.5e}{:15.5e}".format(i, loss, kl, entropy))
|
|
|
|
values = []
|
|
if i == config["num_sgd_iter"] - 1:
|
|
metric_prefix = "policy_gradient/sgd/final_iter/"
|
|
values.append(tf.Summary.Value(
|
|
tag=metric_prefix + "kl_coeff",
|
|
simple_value=kl_coeff))
|
|
else:
|
|
metric_prefix = "policy_gradient/sgd/intermediate_iters/"
|
|
values.extend([
|
|
tf.Summary.Value(
|
|
tag=metric_prefix + "mean_entropy",
|
|
simple_value=entropy),
|
|
tf.Summary.Value(
|
|
tag=metric_prefix + "mean_loss",
|
|
simple_value=loss),
|
|
tf.Summary.Value(
|
|
tag=metric_prefix + "mean_kl",
|
|
simple_value=kl)])
|
|
sgd_stats = tf.Summary(value=values)
|
|
file_writer.add_summary(sgd_stats, global_step)
|
|
global_step += 1
|
|
sgd_time += sgd_end - sgd_start
|
|
if kl > 2.0 * config["kl_target"]:
|
|
kl_coeff *= 1.5
|
|
elif kl < 0.5 * config["kl_target"]:
|
|
kl_coeff *= 0.5
|
|
print("kl div:", kl)
|
|
print("kl coeff:", kl_coeff)
|
|
print("checkpointing time:", checkpointing_time)
|
|
print("rollouts time:", rollouts_time)
|
|
print("shuffle time:", shuffle_time)
|
|
print("load time:", load_time)
|
|
print("sgd time:", sgd_time)
|
|
print("sgd examples/s:", len(trajectory["observations"]) / sgd_time)
|