TF-Agents provides a TensorFlow library for contextual bandits and reinforcement learning.
DI-engine
DI-engine provides an OpenDILab framework for reinforcement learning projects.

About DI-engine
DI-engine is an open-source decision intelligence engine for PyTorch and JAX. It provides Python-first, asynchronous-native task and middleware abstractions and modularly integrates decision-making concepts such as Env, Policy, and Model. The framework supports a wide range of deep reinforcement learning algorithms, including basic DRL, multi-agent RL, imitation learning, offline RL, model-based RL, exploration, LLM + RL, and MCTS + RL through LightZero. It aims to standardize decision intelligence environments and applications for academic research and prototype applications. The project is distributed as a GitHub repository; the source does not state pricing, supported operating systems, or hardware requirements.
Key features
- Supports deep RL algorithms such as DQN, Rainbow, PPO, TD3, SAC, R2D2, and IMPALA.
- Includes multi-agent RL algorithms including QMIX, WQMIX, MAPPO, HAPPO, and ACE.
- Covers imitation learning algorithms like GAIL, SQIL, Guided Cost Learning, and Implicit BC.
- Provides offline RL algorithms such as BCQ, CQL, TD3BC, Decision Transformer, and EDAC.
- Offers model-based RL algorithms including SVG, STEVE, MBPO, DDPPO, and DreamerV3.
- Supports LLM + RL algorithms such as PPO-max, DPO, PromptPG, and PromptAWR.
- Open-source repository with documentation, tutorials, and API reference.
- Includes DI-zoo demonstrations and benchmark environments.
- Supports both academic research and prototype applications.
DI-engine at a glance
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 6, 2026 · Online when checked
- Listed
- Oct 5, 2026
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DI-engine FAQ
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