Exploration is a key component of reinforcement learning (RL). While RL has begun to solve relatively simple tasks, current algorithms cannot complete complex tasks. Our existing algorithms often endlessly dither, failing to meaningfully explore their environments in search of high-reward states. If we hope to have agents autonomously learn increasingly complex tasks, these machines must be equipped with machinery for efficient exploration.

The goal of this workshop is to present and discuss exploration in RL, including deep RL, evolutionary algorithms, real-world applications, and developmental robotics. Invited speakers will share their perspectives on efficient exploration, and researchers will share recent work in spotlight presentations and poster sessions.