Exploration-Guided Reward Shaping for Reinforcement Learning under Sparse Rewards Max Planck Institute for Software Systems (MPI-SWS), Saarbrucken, Germany

We study the problem of reward shaping to accelerate the training process of a reinforcement learning agent. Existing works have considered a number of different reward shaping formulations; however, they either require external domain knowledge or fail in environments with extremely sparse rewards.