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https://github.com/vale981/ray
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60d4d5e1aa
* Remove all __future__ imports from RLlib. * Remove (object) again from tf_run_builder.py::TFRunBuilder. * Fix 2xLINT warnings. * Fix broken appo_policy import (must be appo_tf_policy) * Remove future imports from all other ray files (not just RLlib). * Remove future imports from all other ray files (not just RLlib). * Remove future import blocks that contain `unicode_literals` as well. Revert appo_tf_policy.py to appo_policy.py (belongs to another PR). * Add two empty lines before Schedule class. * Put back __future__ imports into determine_tests_to_run.py. Fails otherwise on a py2/print related error.
66 lines
2.1 KiB
Python
Executable file
66 lines
2.1 KiB
Python
Executable file
"""Example of running a policy server. Copy this file for your use case.
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To try this out, in two separate shells run:
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$ python cartpole_server.py
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$ python cartpole_client.py
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"""
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import os
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from gym import spaces
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import numpy as np
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import ray
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from ray.rllib.agents.dqn import DQNTrainer
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from ray.rllib.env.external_env import ExternalEnv
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from ray.rllib.utils.policy_server import PolicyServer
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from ray.tune.logger import pretty_print
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from ray.tune.registry import register_env
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SERVER_ADDRESS = "localhost"
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SERVER_PORT = 9900
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CHECKPOINT_FILE = "last_checkpoint.out"
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class CartpoleServing(ExternalEnv):
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def __init__(self):
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ExternalEnv.__init__(
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self, spaces.Discrete(2),
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spaces.Box(low=-10, high=10, shape=(4, ), dtype=np.float32))
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def run(self):
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print("Starting policy server at {}:{}".format(SERVER_ADDRESS,
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SERVER_PORT))
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server = PolicyServer(self, SERVER_ADDRESS, SERVER_PORT)
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server.serve_forever()
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if __name__ == "__main__":
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ray.init()
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register_env("srv", lambda _: CartpoleServing())
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# We use DQN since it supports off-policy actions, but you can choose and
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# configure any agent.
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dqn = DQNTrainer(
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env="srv",
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config={
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# Use a single process to avoid needing to set up a load balancer
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"num_workers": 0,
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# Configure the agent to run short iterations for debugging
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"exploration_fraction": 0.01,
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"learning_starts": 100,
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"timesteps_per_iteration": 200,
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})
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# Attempt to restore from checkpoint if possible.
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if os.path.exists(CHECKPOINT_FILE):
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checkpoint_path = open(CHECKPOINT_FILE).read()
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print("Restoring from checkpoint path", checkpoint_path)
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dqn.restore(checkpoint_path)
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# Serving and training loop
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while True:
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print(pretty_print(dqn.train()))
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checkpoint_path = dqn.save()
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print("Last checkpoint", checkpoint_path)
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with open(CHECKPOINT_FILE, "w") as f:
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f.write(checkpoint_path)
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