Welcome to D2C’s documentation!

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 overall framework of D2C is as below:


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

Here are other features of D2C:

  • 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.


D2C interface can be installed as follows:

$ git clone https://github.com/AIR-DI/D2C.git
$ cd d2c
$ pip install -e .


Indices and tables