Recent advancements in model-based reinforcement learning have shown that the dynamics of many structured domains (e.g. DBNs) can be learned with tractable sample complexity, despite their exponentially large state spaces. Unfortunately, these algorithms all require access to a planner that computes a near optimal policy, and while many traditional MDP algorithms make this guarantee, their computation time grows with the number of states. We show how to replace these over-matched planners with a class of sample-based planners — whose computation time is independent of the number of states — without sacrificing the sample-efficiency guarantees of the overall learning algorithms. To do so, we define sufficient criteria for a sample-based planner to be used in such a learning system and analyze two popular sample-based approaches from the literature. We also introduce our own sample-based planner, which combines the strategies from these algorithms and still meets the criteria for integration into our learning system. In doing so, we define the first complete RL solution for compactly represented (exponentially sized) state spaces with efficiently learnable dynamics that is both sample efficient and whose computation time does not grow rapidly with the number of states.

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Tbir paper presents some aspects about measurement odometry, e m r compensation. mobile robot and compensation of the systematic odometry emrs for differential drive platform. The experimental results obtained by running two different UMBmark tests show that systematic calibration can reduce systematic odometry errors more than 10 timer.

This paper examines how the choice of the selection mechanism in an evolutionary algorithm impacts the objective function it optimizes, specifically when the fitness function is noisy. We provide formal results showing that, in an abstract infinite-population model, proportional selection optimizes expected fitness, truncation selection optimizes order statistics, and tournament selection can oscillate. The "winner" in a population depends on the choice of selection rule, especially when fitness distributions differ between individuals resulting in variable risk. These findings are further developed through empirical results on a novel stochastic optimization problem called "Die4", which, while simple, extends existing benchmark problems by admitting a variety of interpretations of optimality.

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