Revisiting Exploration-Conscious Reinforcement Learning
The objective of Reinforcement Learning is to learn an optimal policy by performing actions and observing their long term consequences. Unfortunately, acquiring such a policy can be a hard task. More severely, since one cannot tell if a policy is optimal, there is a constant need for exploration. This is known as the Exploration-Exploitation trade-off. In practice, this trade-off is resolved by using some inherent exploration mechanism, such as the $\epsilon$-greedy exploration, while still trying to learn the optimal policy. In this work, we take a different approach. We define a surrogate optimality objective: an optimal policy with respect to the exploration scheme. As we show throughout the paper, although solving this criterion does not necessarily lead to an optimal policy, the problem becomes easier to solve. We continue by analyzing this notion of optimality, devise algorithms derived from this approach, which reveal connections to existing work, and test them empirically on tabular and deep Reinforcement Learning domains.