In this work, a novel value function-based reinforcement learning (RL) approach, descending dynamic policy programming (DDPP) is proposed to address the issues of sample-efficiency and learning ...

Julia and Python recursion algorithm, fractal geometry and dynamic programming applications including Edit Distance, Knapsack (Multiple Choice), Stock Trading, Pythagorean Tree, Koch Snowflake, Jer ...

Time Series Classification (TSC) aims to develop predictive models for discrete target variables using ordered, real-valued attributes. However, existing deep learning approaches face challenges in ...