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CORR
2012
Springer

Fractional Moments on Bandit Problems

8 years 5 months ago
Fractional Moments on Bandit Problems
Reinforcement learning addresses the dilemma between exploration to find profitable actions and exploitation to act according to the best observations already made. Bandit problems are one such class of problems in stateless environments that represent this explore/exploit situation. We propose a learning algorithm for bandit problems based on fractional expectation of rewards acquired. The algorithm is theoretically shown to converge on an -optimal arm and achieve O(n) sample complexity. Experimental results show the algorithm incurs substantially lower regrets than parameter-optimized -greedy and SoftMax approaches and other low sample complexity state-of-the-art techniques.
Ananda Narayanan B., Balaraman Ravindran
Added 20 Apr 2012
Updated 20 Apr 2012
Type Journal
Year 2012
Where CORR
Authors Ananda Narayanan B., Balaraman Ravindran
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