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Data package

For the article A Reference Architecture of Reinforcement Learning Frameworks.


About

The surge in reinforcement learning (RL) applications gave rise to diverse supporting technology, such as RL frameworks. However, the architectural patterns of these frameworks are inconsistent across implementations and there exist no reference architecture (RA) to form a common basis of comparison, evaluation, and integration. To address this gap, we propose an RA for RL frameworks. By deconstructing and analyzing eighteen state-of-the-practice RL frameworks, we identify recurring architectural components and their relationships, and codify them in an RA. To validate our RA and demonstrate its utility, we reconstruct characteristic RL patterns and existing frameworks. Finally, we identify architectural trends, e.g., commonly used components, implementation strategies, and outline paths to improving RL frameworks. Our work aids RL framework developers in designing and implementing RL services, and helps users in integrating RL components into their applications.

Contents

  • /RA/reference_architecture.pdf - The reference architecture (RA) of RL frameworks.
  • /data/data.xlsx - Data extraction sheet of eighteen RL frameworks.
    • Axial coding - Identifies components and their relationships.
    • Selective coding - Refines component groups and builds reference architecture.
    • Final RA - Finalized reference architecture.
    • RA-to-framework mapping - A view on RA by mapping each RA component to its realization status in RL frameworks.

Color coding

We use the following color codes in Final RA.

  • $\color{Green}{\textsf{Green}}$ - Explicitly implemented component in the specific framework (i.e., standalone component)
  • $\color{Yellow}{\textsf{Yellow}}$ - Explicitly implemented component in the specific framework (i.e., functionality is present but merged into another component)
  • $\color{Grey}{\textsf{Grey}}$ - Explicitly implemented component via an external third-party dependency
  • $\color{Red}{\textsf{Red}}$ - Component not implemented in the specific framework

RA-to-framework mapping includes all components marked in green and yellow, representing both explicitly ($\color{Green}{\textsf{green}}$) and implicitly ($\color{Yellow}{\textsf{yellow}}$) implemented components.

Sampled frameworks

ID Name GitHub repository
F1 Gymnasium https://github.com/Farama-Foundation/Gymnasium
F2 PettingZoo https://github.com/Farama-Foundation/PettingZoo
F3 Unity ML-Agents https://github.com/Unity-Technologies/ml-agents
F4 Isaac Gym https://github.com/isaac-sim/IsaacGymEnvs
F5 Isaac Lab https://github.com/isaac-sim/IsaacLab
F6 dm_control https://github.com/google-deepmind/dm_control
F7 DeepMind Lab https://github.com/google-deepmind/lab
F8 Arcade Learning Environment https://github.com/Farama-Foundation/Arcade-Learning-Environment
F9 Jumanji https://github.com/instadeepai/jumanji
F10 Stable Baselines3 https://github.com/DLR-RM/stable-baselines3
F11 RL Baselines3 Zoo https://github.com/DLR-RM/rl-baselines3-zoo
F12 RLlib https://github.com/ray-project/ray/tree/master/rllib
F13 Acme https://github.com/google-deepmind/acme
F14 MARLlib https://github.com/Replicable-MARL/MARLlib
F15 BenchMARL https://github.com/facebookresearch/BenchMARL
F16 Mava https://github.com/instadeepai/Mava
F17 Dopamine https://github.com/google/dopamine
F18 Tianshou https://github.com/thu-ml/tianshou

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