Accelerating the Computation of UCB and Related Indices for Reinforcement Learning

no code implementations • 28 Sep 2019 • Wesley Cowan, Michael N. Katehakis, Daniel Pirutinsky

In this paper we derive an efficient method for computing the indices associated with an asymptotically optimal upper confidence bound algorithm (MDP-UCB) of Burnetas and Katehakis (1997) that only requires solving a system of two non-linear equations with two unknowns, irrespective of the cardinality of the state space of the Markovian decision process (MDP).

reinforcement-learning Reinforcement Learning (RL)

Reinforcement Learning: a Comparison of UCB Versus Alternative Adaptive Policies

no code implementations • 13 Sep 2019 • Wesley Cowan, Michael N. Katehakis, Daniel Pirutinsky

In this paper we consider the basic version of Reinforcement Learning (RL) that involves computing optimal data driven (adaptive) policies for Markovian decision process with unknown transition probabilities.

reinforcement-learning Reinforcement Learning (RL)