2020/06/04 by Callum Wilson, Annalisa Riccardi, Wilson, Callum +3 · 1 citation
Computer Science · Engineering · #Adaptive Dynamic Programming Control #Advanced Memory and Neural Computing #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning and ELM #Neural and Evolutionary Computing (cs.NE) #Stochastic Gradient Optimization Techniques
paper · pdf · doi:10.48550/arxiv.2006.02986
openalex publication_date 2020/06/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Reinforcement learning is a popular machine learning paradigm which can find\nnear optimal solutions to complex problems. Most often, these procedures\ninvolve function approximation using neural networks with gradient based\nupdates to optimise weights for the problem being considered. While this common\napproach generally works well, there are other update mechanisms which are\nlargely unexplored in reinforcement learning. One such mechanism is Extreme\nLearning Machines. These were initially proposed to drastically improve the\ntraining speed of neural networks and have since seen many applications. Here\nwe attempt to apply extreme learning machines to a reinforcement learning\nproblem in the same manner as gradient based updates. This new algorithm is\ncalled Extreme Q-Learning Machine (EQLM). We compare its performance to a\ntypical Q-Network on the cart-pole task - a benchmark reinforcement learning\nproblem - and show EQLM has similar long-term learning performance to a\nQ-Network.\n