A Deep Reinforcement Learning Blind AI in DareFightingICE
This work addresses the problem of developing AI agents that rely solely on audio inputs for game-playing, which is novel but incremental in the context of reinforcement learning and sound processing.
The paper tackles the challenge of training a deep reinforcement learning agent to play games using only sound input, called a blind AI, on the DareFightingICE platform, and demonstrates its effectiveness along with two proposed metrics for evaluating sound designs in the competition.
This paper presents a deep reinforcement learning agent (AI) that uses sound as the input on the DareFightingICE platform at the DareFightingICE Competition in IEEE CoG 2022. In this work, an AI that only uses sound as the input is called blind AI. While state-of-the-art AIs rely mostly on visual or structured observations provided by their environments, learning to play games from only sound is still new and thus challenging. We propose different approaches to process audio data and use the Proximal Policy Optimization algorithm for our blind AI. We also propose to use our blind AI in evaluation of sound designs submitted to the competition and define two metrics for this task. The experimental results show the effectiveness of not only our blind AI but also the proposed two metrics.