In this tutorial, we code a mini reinforcement learning setup in which a multi-agent system learns to navigate a grid world through interaction, feedback, and layered decision-making. We build everything from scratch and bring together three agent roles: an Action Agent, a Tool Agent, and a Supervisor, so we can observe how simple heuristics, analysis,
The post How to Design a Mini Reinforcement Learning Environment-Acting Agent with Intelligent Local Feedback, Adaptive Decision-Making, and Multi-Agent Coordination appeared first on MarkTechPost. Read More
