James's ML@B

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EECS + Math | Sophomore
ude.yelekreb.lm@semaj

I am an undergraduate machine learning researcher working on problems in model-free and model-based reinforcement learning. I have also done research in the time series domain, and generative methods such as GANs.

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  • James

    James wrote an update for Non-linear Dynamics Learning via Neural Networks.

    We have been working on testing aDOBO on a toy environment, called Dubins car, where you have to control a car in 3 dimensions to move it to a goal location. We are also working on looking into the efficacy of aDOBO in comparison to model-free approaches. Additionally, we are going to be looking at extending aDOBO to allow for efficient learning of multiple goals. For example, say we wanted to be able to effectively learn how to drive our car not just to one goal location but to any goal location, in a typical model-based RL approach this would be very easy because model-based approaches allow for changing of the cost function without re-learning the dynamics model. However, in the aDOBO framework, we learn the dynamics in a goal-driven way meaning we must re-learn the dynamics whenever we change the cost function, so we are looking into ways to extend aDOBO to avoid this problem.