Scaling Model-Based Average-Reward Reinforcement Learning for Product Delivery

Reinforcement learning in real-world domains suffers from three curses of dimensionality: explosions in state and action spaces, and high stochasticity. We present approaches that mitigate each of these curses. To handle the state-space explosion, we introduce "tabular linear functions" that generalize tile-coding and linear value functions. Action space complexity is reduced by replacing complete joint action space search with a form of hill climbing. To deal with high stochasticity, we introduce a new algorithm called ASH-learning, which is an afterstate version of H-Learning. Our extensions make it practical to apply reinforcement learning to a domain of product delivery - an optimization problem that combines inventory control and vehicle routing.

• Type: Conference
• Year: 2006
• Where: ECML
• Authors: Scott Proper, Prasad Tadepalli
• Keywords: Action Space, ECML 2006, Machine Learning, Real-world Domains Suffers, Reinforcement Learning