Ray RLlib is an open-source reinforcement-learning library for production-level, highly distributed workloads. It keeps APIs unified and simple while supporting industry applications such as decentralized mobile-network coordination. section 12.4
A practical RLlib workflow includes:
- Install Ray RLlib with pip and register a wrapped Gymnasium environment. section 12.4
- Initialize Ray, configure PPO, allocate rollout workers, and define shared or mapped multi-agent policies. section 12.4
- Train with Ray Tune, stop after a chosen iteration limit, checkpoint the result, and select the highest-reward run. section 12.4
- Restore the checkpoint, compute actions for each agent, evaluate the environment, and render coordination results. section 12.4
Multi-agent mobile-network coordination
Five agents control cell selection for five users across three stations in a small mobile environment. PPO trains the shared policy, and evaluation renders the resulting connections, average data rate, and utility. section 12.4
Figure 12.11 Coordination of the mobile environment using a multi-agent PPO
Use the rendered coordination figure to inspect how the trained multi-agent policy connects users with base stations. section 12.4
