Multi-agent Social Learning

Project Goals: The goal of the project is to develop an algorithm for iteratively constructing recursive hierarchies of options. The hypothesis is that such a method could have the potential to achieve an exponential improvement over flat reinforcement learning policies in learning efficiency by exploring with high-level option primitives in addition to low-level actions. Our proposed algorithm works similarly to tabulation methods for dynamic programming, which iteratively fill a repertoire of subsolutions to subproblems that are then reused for more complex problems.