FlashRL: A Reinforcement Learning Platform for Flash Games

TL;DR

This paper introduces FlashRL, a new platform that enables reinforcement learning research on thousands of Flash games, facilitating experimentation and advancing RL algorithms in a previously underexplored domain.

Contribution

The paper presents FlashRL, the first comprehensive platform for RL research on Flash games, addressing previous limitations in task diversity and accessibility.

Findings

Platform supports thousands of Flash games.

Achieves high performance with minimal CPU usage.

Shows promising results for new RL algorithms.

Abstract

Reinforcement Learning (RL) is a research area that has blossomed tremendously in recent years and has shown remarkable potential in among others successfully playing computer games. However, there only exists a few game platforms that provide diversity in tasks and state-space needed to advance RL algorithms. The existing platforms offer RL access to Atari- and a few web-based games, but no platform fully expose access to Flash games. This is unfortunate because applying RL to Flash games have potential to push the research of RL algorithms. This paper introduces the Flash Reinforcement Learning platform (FlashRL) which attempts to fill this gap by providing an environment for thousands of Flash games on a novel platform for Flash automation. It opens up easy experimentation with RL algorithms for Flash games, which has previously been challenging. The platform shows excellent performance with as little as 5% CPU utilization on consumer hardware. It shows promising results for novel reinforcement learning algorithms.