  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
eta-optimal arm and achieve O(n) sample complexity. Experimental results show
the algorithm incurs substantially lower regrets than parameter-optimized
eta-greedy and SoftMax approaches and other low sample complexity
state-of-the-art techniques.
