Sciweavers

Share
JUCS
2007

Focus of Attention in Reinforcement Learning

11 years 9 months ago
Focus of Attention in Reinforcement Learning
Abstract: Classification-based reinforcement learning (RL) methods have recently been proposed as an alternative to the traditional value-function based methods. These methods use a classifier to represent a policy, where the input (features) to the classifier is the state and the output (class label) for that state is the desired action. The reinforcement-learning community knows that focusing on more important states can lead to improved performance. In this paper, we investigate the idea of focused learning in the context of classification-based RL. Specifically, we define a useful notation of state importance, which we use to prove rigorous bounds on policy loss. Furthermore, we show that a classification-based RL agent may behave arbitrarily poorly if it treats all states as equally important. Key Words: reinforcement learning, function approximation, generalization, attention. Category: I.2.6 [Artificial Intelligence]: Learning
Lihong Li, Vadim Bulitko, Russell Greiner
Added 16 Dec 2010
Updated 16 Dec 2010
Type Journal
Year 2007
Where JUCS
Authors Lihong Li, Vadim Bulitko, Russell Greiner
Comments (0)
books