Model-based Reinforcement Learning

We study the problem of action sampling and propose a method to incorporate equivariance properties to the action sampling procedure.

We study E(2) Euclidean equivariance in navigation on geometric graphs and develop message passing network to solve it.

We study whether Euclidean symmetry can help in reinforcement learning and planning, which models the geometric transformations between reference frames of robots.

We formulate how differentiable planning algorithms can exploit inherent symmetry in path planning problems, named SymPlan, and propose practical algorithms.

We study how implicit differentiation helps scale up and improve convergence of differentiable planning algorithms.