Dopamine: A Research Framework for Deep Reinforcement Learning

TL;DR

Dopamine is an open-source, TensorFlow-based framework designed to facilitate diverse deep reinforcement learning research by providing reliable implementations of state-of-the-art agents and a taxonomy of research objectives.

Contribution

The paper introduces Dopamine, a new flexible framework for deep RL research, and offers a taxonomy to categorize research goals in the field.

Findings

Provides reliable implementations of deep RL agents

Highlights the diversity of research objectives in deep RL

Supports reproducibility and benchmarking in deep RL

Abstract

Deep reinforcement learning (deep RL) research has grown significantly in recent years. A number of software offerings now exist that provide stable, comprehensive implementations for benchmarking. At the same time, recent deep RL research has become more diverse in its goals. In this paper we introduce Dopamine, a new research framework for deep RL that aims to support some of that diversity. Dopamine is open-source, TensorFlow-based, and provides compact and reliable implementations of some state-of-the-art deep RL agents. We complement this offering with a taxonomy of the different research objectives in deep RL research. While by no means exhaustive, our analysis highlights the heterogeneity of research in the field, and the value of frameworks such as ours.