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cs.LG2024
Provably Efficient Long-Horizon Exploration in Monte Carlo Tree Search through State Occupancy Regularization
Liam Schramm, Abdeslam Boularias
Monte Carlo tree search (MCTS) has been successful in a variety of domains, but faces challenges with long-horizon exploration when compared to sampling-based motion planning algor…
cs.LG2024
Bellman Diffusion Models
Liam Schramm, Abdeslam Boularias
Diffusion models have seen tremendous success as generative architectures. Recently, they have been shown to be effective at modelling policies for offline reinforcement learning a…