Reinforcement Learning and Automated Negotiation - A match made in gymnasium

Reinforcement learning is probably one of the most successful ideas in the history of computing. It allowed us lowly computer geeks to build agents that beat or at least rival world champions in chess, go, backgammon, poker, Atari games, and, coolest of all, starcraft. It has its role in the rise of generative AIs like ChatGPT through RLHL (Reinforcement Learning from Human Feedback). Nevertheless, RL is still far from achieving its full potential in the real world. One reason is that the world is messy, much messier than these games with well-defined rules. In this post, I will introduce you to a new game for RL that is directly applicable to the real world. Find a solution today, apply it tomorrow. This new game is technically challenging, just at the edge of the current state of the art in RL and multiagent RL research. If you are looking for a new challenge that is more than dogs vs. cats, look no further.