Heuristically-Accelerated Reinforcement Learning: A Comparative Analysis of Performance [chapter]

Murilo Fernandes Martins, Reinaldo A. C. Bianchi
2014 Lecture Notes in Computer Science  
This paper presents a comparative analysis of three Reinforcement Learning algorithms (Q-learning, Q(λ)-learning and QSlearning) and their heuristically-accelerated variants (HAQL, HAQ(λ) and HAQS) where heuristics bias action selection, thus speeding up the learning. The experiments were performed in a simulated robot soccer environment which reproduces the conditions of a real competition league environment. The results clearly demonstrate that the use of heuristics substantially improves the performance of the learning algorithms.
doi:10.1007/978-3-662-43645-5_2 fatcat:i6ayghqymncppdb4ujyil3k5zi