Exploration In Feature Space For Reinforcement Learning

Abstract

The infamous exploration-exploitation dilemma is one of the oldest and most important problems in reinforcement learning (RL). Deliberate and effective exploration is necessary for RL agents to succeed in most environments. However, until very recently even very sophisticated RL algorithms employed simple, undirected exploration strategies in large-scale RL tasks. We introduce a new optimistic count-based exploration algorithm for RL that is feasible in high-dimensional MDPs. The success of RL algorithms in these domains depends crucially on generalization from limited training experience. Function approximation techniques enable RL agents to generalize in order to estimate the value of unvisited states, but at present few methods have achieved generalization about the agent's uncertainty regarding unvisited states. We present a new method for computing a generalized state visit-count, which allows the agent to estimate the uncertainty associated with any state. In contrast to exis

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