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Planning on Infinite Completion Trees Using Markovian Multi Armed Bandits
Abstract: Task and Motion Planning (TAMP) requires generating symbolic task plans together with feasible motion plans, making it necessary to reason jointly about discrete actions, continuous motion feasibility, and the computational effort required to evaluate candidate plans. Because geometric checks are often expensive and interdependent, a TAMP solver must effectively manage the order in which… Continue Reading Planning on Infinite Completion Trees Using Markovian Multi Armed Bandits
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Separating Signal from Noise: Denoised World Models for Generalization in Reinforcement Learning
Abstract: A central challenge in reinforcement learning is generalization: agents trained on a set of tasks often fail catastrophically when deployed on new ones, even when the underlying structure is approximately shared. We hypothesize that one key reason for this failure is spurious correlations: during training the agent connects exogenous details (e.g. colors, backgrounds, and… Continue Reading Separating Signal from Noise: Denoised World Models for Generalization in Reinforcement Learning