Beyond The Rainbow: High Performance Deep Reinforcement Learning on a Desktop PC
This work addresses the problem of computational efficiency and accessibility in reinforcement learning for researchers and practitioners, representing an incremental advancement by building on existing methods.
The paper tackles the challenge of achieving high-performance deep reinforcement learning on a desktop PC by introducing the 'Beyond The Rainbow' (BTR) algorithm, which integrates six improvements to Rainbow DQN, resulting in a new state-of-the-art human-normalized IQM of 7.4 on Atari-60 and successful training on complex 3D games like Super Mario Galaxy.
Rainbow Deep Q-Network (DQN) demonstrated combining multiple independent enhancements could significantly boost a reinforcement learning (RL) agent's performance. In this paper, we present "Beyond The Rainbow" (BTR), a novel algorithm that integrates six improvements from across the RL literature to Rainbow DQN, establishing a new state-of-the-art for RL using a desktop PC, with a human-normalized interquartile mean (IQM) of 7.4 on Atari-60. Beyond Atari, we demonstrate BTR's capability to handle complex 3D games, successfully training agents to play Super Mario Galaxy, Mario Kart, and Mortal Kombat with minimal algorithmic changes. Designing BTR with computational efficiency in mind, agents can be trained using a high-end desktop PC on 200 million Atari frames within 12 hours. Additionally, we conduct detailed ablation studies of each component, analyzing the performance and impact using numerous measures. Code is available at https://github.com/VIPTankz/BTR.