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We will have two talks describing recent developments by the group. First, we will present a Bayesian solution to the problem of estimating posterior distributions of simulation parameters given real data. The uncertainty captured in the posterior can significantly improve the performance of reinforcement learning algorithms trained in simulation but deployed in the real world. We will also show that combining posterior parameter estimation and policy updates sequentially leads to further improvements on the convergence rate.
In the second part, we will address the problem of mapping as an online classification problem. We will show that optimal transport can be a valuable theoretical framework to enable fast transformation of geometric information obtained in an environment or simulated environment into a secondary domain, leveraging prior information in an elegant and efficient manner.
Author Information
Fabio Ramos (University of Sydney, NVIDIA)
Anthony Tompkins (The University of Sydney)
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