Dopamine: A Research Framework for Deep Reinforcement Learning

Pablo Samuel Castro, Subhodeep Moitra, Carles Gelada, Saurabh Kumar, Marc G. Bellemare

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 yet 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.

Keywords: reinforcement learning, software, framework, reproducibility

TL;DR: In this paper we introduce Dopamine, a new research framework for deep RL that is open-source, TensorFlow-based, and provides compact yet reliable implementations of some state-of-the-art deep RL agents.

Community Implementations: [ 9 code implementations](https://www.catalyzex.com/paper/arxiv:1812.06110/code)