با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
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Principled reward shaping for reinforcement learning via lyapunov stability theory
شکل دادن پاداش اصولی برای یادگیری تقویتی از طریق تئوری پایداری لیاپونوف-2020 Reinforcement learning (RL) suffers from the designation in reward function and the large computational iterating steps until convergence. How to accelerate the training process in RL plays a vital role. In this paper, we proposed a Lyapunov function based approach to shape the reward function which can effec- tively accelerate the training. Furthermore, the shaped reward function leads to convergence guarantee via stochastic approximation, an invariant optimality condition using Bellman Equation and an asymp- totical unbiased policy. Moreover, sufficient RL benchmarks have been experimented to demonstrate the effectiveness of our proposed method. It has been verified that our proposed method substantially accel- erates the convergence process as well as improves the performance in terms of a higher accumulated reward. Keywords: Reinforcement learning | Principled reward shaping | Lyapunov stability theory | Stochastic approximation |
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