Dopamine: A Research Framework for Deep Reinforcement Learning (1812.06110v1)

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.

• The paper introduces Dopamine, a concise framework implementing four key value-based agents for deep reinforcement learning.
• It emphasizes simplicity and reproducibility, offering over 98% test coverage and easy hyperparameter management via gin-config.
• The framework promotes standardized evaluation and instructional utility, enabling rapid experimentation and clear algorithmic insights.

Dopamine: A Research Framework for Deep Reinforcement Learning

The paper introduces Dopamine, an open-source research framework tailored for deep reinforcement learning (RL) with a particular emphasis on value-based reinforcement learning methodologies. Built upon TensorFlow, this framework offers compact and reliable implementations of state-of-the-art deep RL agents, aiming to fill a unique niche in the burgeoning ecosystem of RL research tools.

Overview and Contributions

Dopamine distinguishes itself by prioritizing simplicity and compactness, consisting of merely 12 Python files and approximately 2000 lines of code. This minimalistic approach ensures that researchers can swiftly comprehend and modify the framework without being overwhelmed by unnecessary complexity. The framework's architecture encompasses several core components, including agents, checkpointers, loggers, and runners, facilitating the management of interactions between agents and their environments.

The framework initially provides implementations for four distinct value-based agents, namely DQN, C51, Rainbow, and IQN, each encompassing nuanced variations in the application of deep Q-network architectures. The design choices encapsulated within Dopamine target algorithmic research and instructional purposes, positing simplicity as a boon for experimentation and clarity, especially for newcomers to the field.

Reliable and Reproducible Framework

Reliability and reproducibility are central tenets of the Dopamine framework. It provides comprehensive tests with a code coverage exceeding 98%, reinforcing the accuracy and dependability of the framework. The adoption of gin-config for parameter management further enhances the reproducibility and customization of experiments, allowing researchers to effortlessly adjust hyperparameters via configuration files.

The paper underscores the framework's novel approach to standardization by establishing a consistent set of hyperparameters for evaluating agent performance. This uniformity is not intended to suggest optimality but rather encourages a baseline for comparison, promoting transparency and facilitating reproducible research outcomes. Dopamine's utility is illustrated through several case studies, demonstrating the impacts of critical parameters such as episode termination strategies and sticky actions.

Implications for Deep Reinforcement Learning Research

Dopamine’s contribution extends beyond its initial design, reflecting profound implications for future research avenues in deep RL. The framework's commitment to simplicity empowers researchers to explore algorithmic innovations with ease, potentially unveiling new directions in the RL domain that may otherwise have been obscured by more complex frameworks. Moreover, its focus on instructional utility aids the dissemination of RL methodologies within the broader research community, establishing a pedagogical scaffold for understanding deep RL's intricacies.

Future Developments

The authors hint at possible future expansions to encompass policy-based methods and more diverse environments beyond the Arcade Learning Environment. They also express cautious consideration of distributed methods, aiming not to compromise the framework's simplicity, maintaining its accessibility for broad research engagement.

In conclusion, Dopamine stands out as a carefully crafted tool tailored to the nuanced requirements of deep RL research. It offers a balance between simplicity and functionality, fostering a transparent and reproducible research environment. By addressing specific needs within the RL community, Dopamine holds the potential to catalyze future innovations and enhance the educational landscape of reinforcement learning research.