# Learning-Driven Exploration for Reinforcement Learning

## TL;DR

This paper introduces entropy-based exploration (EBE), a novel method for reinforcement learning that adaptively explores the state space based on learning progress, leading to faster and more efficient training.

## Contribution

The paper proposes EBE, an entropy-based exploration strategy that uses state-dependent action values to guide exploration without hyperparameter tuning.

## Key findings

- EBE enables more efficient exploration in diverse environments.
- Agents using EBE learn faster compared to traditional heuristics.
- The method does not require hyperparameter tuning.

## Abstract

Effective and intelligent exploration has been an unresolved problem for reinforcement learning. Most contemporary reinforcement learning relies on simple heuristic strategies such as $\epsilon$-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}.