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Learning and planning in partially bservable Markov decision processes (POMDPs) is computationally intractable in real-time system. In order to address this problem, this paper proposes a belief policy reuse (BPR) method to avoid repeated computation. Firstly, the policy reuse evaluation mechanism based on belief Kullback¨CLeibler divergence is presented as a similarity metric between beliefs in the...
Bayesian reinforcement learning provides an elegant solution to the optimal tradeoff between exploration and exploitation of the uncertainty in learning. Unfortunately, the size of the learning parameters grows exponentially with the problem horizon. In this paper, we propose a novel Monte Carlo tree search for Bayesian reinforcement learning approach using a compact factored representation, to solve...
Monte-Carlo tree search (MCTS) combines the generality of stochastic simulation and the accuracy of tree search, which has attracted the great attention of scholars. However, the MCTS search requires a sufficient number of iterations to converge to a good solution, which is more difficult to optimize. In order to solve this problem, this paper presents a point-based incremental pruning (PIP) for Monte-Carlo...
Segmentation and tracking of multiple humans in crowded situations is made difficult by interobject occlusion. We propose a model-based approach to interpret the image observations by multiple partially occluded human hypotheses in a Bayesian framework. We define a joint image likelihood for multiple humans based on the appearance of the humans, the visibility of the body obtained by occlusion reasoning,...
In this paper, we present a probabilistic framework for automatic detection and tracking of objects. We address the data association problem by formulating the visual tracking as finding the best partition of a measurement graph containing all detected moving regions. In order to incorporate model information in tracking procedure, the posterior distribution is augmented with Adaboost image likelihood...
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