CONF dimitrakakis:icann:2006/IDIAP Nearly optimal exploration-exploitation decision thresholds Dimitrakakis, Christos EXTERNAL http://publications.idiap.ch/attachments/papers/2006/dimitrakakis-icann-2006.pdf PUBLIC http://publications.idiap.ch/index.php/publications/showcite/dimitrakakis:rr06-12 Related documents Int. Conf. on Artificial Neural Networks (ICANN) 2006 IDIAP-RR 06-12 While in general trading off exploration and exploitation in reinforcement learning is hard, under some formulations relatively simple solutions exist. Optimal decision thresholds for the multi-armed bandit problem, one for the infinite horizon discounted reward case and one for the finite horizon undiscounted reward case are derived, which make the link between the reward horizon, uncertainty and the need for exploration explicit. From this result follow two practical approximate algorithms, which are illustrated experimentally. REPORT dimitrakakis:rr06-12/IDIAP Nearly optimal exploration-exploitation decision thresholds Dimitrakakis, Christos EXTERNAL http://publications.idiap.ch/attachments/reports/2006/dimitrakakis-idiap-rr-06-12.pdf PUBLIC Idiap-RR-12-2006 2006 IDIAP To appear in ICANN 2006 While in general trading off exploration and exploitation in reinforcement learning is hard, under some formulations relatively simple solutions exist. Optimal decision thresholds for the multi-armed bandit problem, one for the infinite horizon discounted reward case and one for the finite horizon undiscounted reward case are derived, which make the link between the reward horizon, uncertainty and the need for exploration explicit. From this result follow two practical approximate algorithms, which are illustrated experimentally.