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  1. This paper addresses the problem of learning the optimal control policy for a nonlinear stochastic dynam- ical. This problem is subject to the ‘curse of dimension- ality’ associated with the dynamic programming method. This paper proposes a novel decoupled data-based con- trol (D2C) algorithm that addresses this problem using a decoupled, ‘open-loop - closed-loop’, approach. First, an open-loop deterministic trajectory optimization problem is solved using a black-box simulation model of the dynamical system. Then, closed-loop control is developed around this open-loop trajectory by linearization of the dynamics about this nominal trajectory. By virtue of linearization, a linear quadratic regulator based algorithm can be used for this closed-loop control. We show that the performance of D2C algorithm is approximately optimal. Moreover, simulation performance suggests a significant reduction in training time compared to other state of the art algorithms. 
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  3. This paper presents a new recursive Hybrid consensus filter for distributed state estimation on a Hidden Markov Model (HMM), which is well suited to multirobot applications and settings. The proposed algorithm is scalable, robust to network failure and capable of handling non-Gaussian transition and observation models and is, therefore, quite general. No global knowledge of the communication network is assumed. Iterative Conservative Fusion (ICF) is used to reach consensus over potentially correlated priors, while consensus over likelihoods is handled using weights based on a Metropolis Hastings Markov Chain (MHMC). The proposed method is evaluated in a multi-agent tracking problem and a high-dimensional HMM and it is shown that its performance surpasses the competing algorithms. 
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