RLlib: Production-grade deep reinforcement learning

As we mentioned at the beginning, one of the motivations of Ray's creators is to build an easy-to-use distributed computing framework that can handle complex and heterogenous applications such as deep reinforcement learning. With that, they also created a widely-used deep RL library based on Ray. Training a model similar to ours is very simple using RLlib. The main steps are:

• Import the default training configs for Ape-X DQN as well as the trainer,
• Customize the training configs,
• Train the trainer.

That's it! The code necessary for that is very simple. All you need is the following:

Chapter06/rllib_apex_dqn.py

import pprint
from ray import tune
from ray.rllib.agents.dqn.apex import APEX_DEFAULT_CONFIG
from ray.rllib.agents.dqn.apex import ApexTrainer
if __name__ == '__main__':
    config = APEX_DEFAULT_CONFIG.copy()
    pp = pprint.PrettyPrinter...