Deterministic Sequencing of Exploration and Exploitation for Reinforcement Learning

We propose Deterministic Sequencing of Exploration and Exploitation (DSEE) algorithm with interleaving exploration and exploitation epochs for model-based RL problems that aim to simultaneously learn the system model, i.e., a Markov decision process (MDP), and the associated optimal policy. During exploration, DSEE explores the environment and updates the estimates for expected reward and transition probabilities. During exploitation, the latest estimates of the system dynamics are used to obtain a robust policy with high probability. We design the lengths of the exploration and exploitation epochs such that the cumulative regret grows as a sub-linear function of time. We also discuss a method for efficient exploration using multi-hop MDP and Metropolis-Hastings algorithm to uniformly sample each state-action pair with high probability.