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Reinforcement learning has gotten a lot of attention recently, thanks in large part to systems like AlphaGo and AlphaZero, which have highlighted its immense potential in dramatic ways. And while the RL systems we\\u2019ve developed have accomplished some impressive feats, they\\u2019ve done so in a fairly naive way. Specifically, they haven\\u2019t tended to confront multi-agent problems, which require collaboration and competition. But even when multi-agent problems have been tackled, they\\u2019ve been addressed using agents that just assume other agents are an uncontrollable part of the environment, rather than entities with rich internal structures that can be reasoned and communicated with.

That\\u2019s all finally changing, with new research into the field of multi-agent RL, led in part by OpenAI, Oxford and Google alum, and current FAIR research scientist Jakob Foerster. Jakob\\u2019s research is aimed specifically at understanding how reinforcement learning agents can learn to collaborate better and navigate complex environments that include other agents, whose behavior they try to model. In essence, Jakob is working on giving RL agents a theory of mind.