Data-driven Control Library

2022-Present

D2C (Data-driven Control Library) is a library for data-driven decision-making & control based on state-of-the-art offline reinforcement learning (RL), offline imitation learning (IL), and offline planning algorithms. It is a platform for solving various decision-making & control problems in real-world scenarios. D2C is designed to offer fast and convenient algorithm performance development and testing, as well as providing easy-to-use toolchains to accelerate the real-world deployment of SOTA data-driven decision-making methods.

The current supported offline RL/IL algorithms include (more to come):

• Twin Delayed DDPG with Behavior Cloning (TD3+BC)
• Distance-Sensitive Offline Reinforcement Learning (DOGE)
• Dynamics-Aware Hybrid Offline-and-Online Reinforcement Learning (H2O)
• Sparse Q-learning (SQL)
• Policy-guided Offline RL (POR)
• Offline Reinforcement Learning with Implicit Q-Learning (IQL)
• Discriminator-Guided Model-Based Offline Imitation Learning (DMIL)
• Behavior Cloning (BC)

Features:

• D2C includes a large collection of offline RL and IL algorithms: model-free and model-based offline RL/IL algorithms, as well as planning methods.

• D2C is highly modular and extensible. You can easily build custom algorithms and conduct experiments with it.

• D2C automates the development process in real-world control applications. It simplifies the steps of problem definition/mathematical formulation, policy training, policy evaluation and model deployment.

Library Information

• The library is available at https://github.com/AIR-DI/D2C.
• The tutorials and API documentation are hosted on air-d2c.readthedocs.io.