This is a presentation I gave to the Journal Club of the Nakamura Laboratory while I was interning there, before my graduate studies. We discussed the the paper Continuous Inverse Optimal Control with Locally Optimal Examples by Levine and Koltun. It is a work in inverse optimal control, a.k.a. inverse reinforcement learning, which is the problem of deriving an unknown reward function from demonstrations of the optimal policy in a Markov decision process.
|Maximum Entropy Inverse Reinforcement Learning|
|Continuous Inverse Optimal Control with Locally Optimal Examples|
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