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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
"""Example of querying a policy server. Copy this file for your use case.
To try this out, in two separate shells run:
$ python cartpole_server.py
$ python cartpole_client.py
"""
import argparse
import gym
from ray.rllib.utils.policy_client import PolicyClient
parser = argparse.ArgumentParser()
parser.add_argument(
"--no-train", action="store_true", help="Whether to disable training.")
parser.add_argument(
"--off-policy",
action="store_true",
help="Whether to take random instead of on-policy actions.")
parser.add_argument(
"--stop-at-reward",
type=int,
default=9999,
help="Stop once the specified reward is reached.")
if __name__ == "__main__":
args = parser.parse_args()
env = gym.make("CartPole-v0")
client = PolicyClient("http://localhost:9900")
eid = client.start_episode(training_enabled=not args.no_train)
obs = env.reset()
rewards = 0
while True:
if args.off_policy:
action = env.action_space.sample()
client.log_action(eid, obs, action)
else:
action = client.get_action(eid, obs)
obs, reward, done, info = env.step(action)
rewards += reward
client.log_returns(eid, reward, info=info)
if done:
print("Total reward:", rewards)
if rewards >= args.stop_at_reward:
print("Target reward achieved, exiting")
exit(0)
rewards = 0
client.end_episode(eid, obs)
obs = env.reset()
eid = client.start_episode(training_enabled=not args.no_train)