IFSA: incremental feature-set augmentation for reinforcement learning tasks

Reinforcement learning is a popular and successful framework for many agent-related problems because only limited environmental feedback is necessary for learning. While many algorithms exist to learn effective policies in such problems, learning is often used to solve real world problems, which typically have large state spaces, and therefore suffer from the “curse of dimensionality.” One effective method for speeding-up reinforcement learning algorithms is to leverage expert knowledge. In this paper, we propose a method for dynamically augmenting the agent’s feature set in order to speed up value-function-based reinforcement learning. The domain expert divides the feature set into a series of subsets such that a novel problem concept can be learned from each successive subset. Domain knowledge is also used to order the feature subsets in order of their importance for learning. Our algorithm uses the ordered feature subsets to learn tasks significantly faster than if the entir...

• Type: Conference
• Year: 2007
• Where: ATAL
• Authors: Mazda Ahmadi, Matthew E. Taylor, Peter Stone
• Keywords: ATAL 2007, Feature Set, Reinforcement Learning, Reinforcement Learning Algorithms