Learning-Driven Exploration for Reinforcement Learning
This addresses the challenge of intelligent exploration for reinforcement learning agents, offering a novel method that improves training efficiency, though it is incremental as it builds on existing exploration concepts.
The paper tackles the problem of inefficient exploration in reinforcement learning by introducing entropy-based exploration (EBE), which quantifies learning using state-dependent action values to adaptively explore state space, resulting in faster learning without hyperparameter tuning across diverse environments.
Effective and intelligent exploration has been an unresolved problem for reinforcement learning. Most contemporary reinforcement learning relies on simple heuristic strategies such as $ε$-greedy exploration or adding Gaussian noise to actions. These heuristics, however, are unable to intelligently distinguish the well explored and the unexplored regions of state space, which can lead to inefficient use of training time. We introduce entropy-based exploration (EBE) that enables an agent to explore efficiently the unexplored regions of state space. EBE quantifies the agent's learning in a state using merely state-dependent action values and adaptively explores the state space, i.e. more exploration for the unexplored region of the state space. We perform experiments on a diverse set of environments and demonstrate that EBE enables efficient exploration that ultimately results in faster learning without having to tune any hyperparameter. The code to reproduce the experiments is given at \url{https://github.com/Usama1002/EBE-Exploration} and the supplementary video is given at \url{https://youtu.be/nJggIjjzKic}.