Multi-agent reinforcement learning

As explained in Optimization Algorithms: AI techniques for design, planning, and control problems

Multi-agent reinforcement learning is reinforcement learning in which multiple agents learn or execute policies while interacting within a shared environment.

Multi-agent reinforcement learning coordinates several reinforcement-learning agents that act in parallel within one environment. Each agent can control part of a larger system, enabling more distributed and decentralized control than a single central policy. section 12.4

A mobile-network example assigns one agent to each user's cell-selection task. Five agents operate across stations and users, while shared rewards reflect network performance such as data rate and utility. Ray RLlib wraps the environment, trains the agents with PPO, optionally maps them to one shared policy, and restores the best checkpoint for execution. section 12.4

Coordinating mobile users

Create the mobile-small-ma-v0 environment, register its RLlib wrapper, configure PPO, and assign every agent to shared_policy. During execution, compute one action per agent, step the environment with the action dictionary, and render the changing connections. Training can stop after a configured iteration limit, then select the checkpoint with the highest mean episode reward. section 12.4

Figure 12.10 The mobile-env environment with a number of base stations and user equipment

Use the mobile-environment figure to inspect how multiple base stations, users, and dynamically changing connections form a coordination problem. section 12.4

How Optimization Algorithms: AI techniques for design, planning, and control problems covers it

How deep it goes

Implements environment wrapping, shared-policy mapping, PPO training, checkpoint selection, and per-agent inference without developing algorithm internals.

Examples

Uses mobile-network cell selection, traffic signals, freight delivery, taxi dispatch, and elevator dispatch as coordination problems.

What you can do after

Apply it via Ray RLlib to train, select, restore, and run coordinated PPO agents in a multi-agent environment.

How it connects

Connects reinforcement learning with PPO, decentralized control, policy sharing, traffic signal control, and dispatch optimization.

liveBookmanning.com

Read Optimization Algorithms: AI techniques for design, planning, and control problems on liveBook Get the book at manning.com

Related concepts

• reinforcement learningkind of · Reinforcement learning is a machine-learning paradigm in which an agent learns actions or a policy through interaction with an environment and evaluative feedback, such as rewards.

This explainer was generated by Manning's knowledge graph from the text of Optimization Algorithms: AI techniques for design, planning, and control problems. The section links point to the parts of the book it draws on.