Rainbow: Combining Improvements in Deep Reinforcement Learning

TL;DR

This paper investigates the combination of six improvements to the DQN algorithm in deep reinforcement learning, demonstrating that their integration achieves state-of-the-art results on Atari 2600 benchmarks.

Contribution

It systematically studies the complementarity of multiple DQN enhancements and empirically demonstrates their combined effectiveness in improving performance.

Findings

Achieves state-of-the-art Atari 2600 performance

Shows the combined approach improves data efficiency

Provides ablation analysis of each component's contribution

Abstract

The deep reinforcement learning community has made several independent improvements to the DQN algorithm. However, it is unclear which of these extensions are complementary and can be fruitfully combined. This paper examines six extensions to the DQN algorithm and empirically studies their combination. Our experiments show that the combination provides state-of-the-art performance on the Atari 2600 benchmark, both in terms of data efficiency and final performance. We also provide results from a detailed ablation study that shows the contribution of each component to overall performance.