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Information Particle Filter Tree: An Online Algorithm for POMDPs with Belief-Based Rewards on Continuous Domains
Johannes Fischer*, Ömer Şahin Taş* ICML, 2020 paper / supplemental / code / video IPFT solves continuous ρPOMDPs by combining MCTS with particle-based belief updates. It integrates an information-theoretic shaping term to accelerate search in problems where gathering information is crucial for optimal decision making. |
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Efficient Sampling in POMDPs with Lipschitz Bandits for Motion Planning in Continuous Spaces
Ömer Şahin Taş, Felix Hauser, Martin Lauer IEEE Intelligent Vehicles Symposium (IV), 2021 arXiv / slides / video Lipschitz-based bandits exploit the continuity of Q-values. We harness this smoothness in sampling-based POMDPs to accelerate search in large discrete action sets. Furthermore, we refine the bandits with variance-based updates to handle local discontinuities. |