# Learning-Driven Exploration for Reinforcement Learning

arXiv: https://arxiv.org/abs/1906.06890 · HTML: https://mlsanity.com/paper/1906.06890/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jun 2019.

## Built on by (most cited first, 2 of 2)

- [Exploration in Deep Reinforcement Learning: A Survey](https://mlsanity.com/paper/2205.00824/) (2022)
- [Hybrid Action Based Reinforcement Learning for Multi-Objective Compatible Autonomous Driving](https://mlsanity.com/paper/2501.08096/) (2025)

## Abstract (arXiv)

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 urlhttps://github.com/Usama1002/EBE-Exploration and the supplementary video is given at urlhttps://youtu.be/nJggIjjzKic.