AI-Optimizer is a free, open-source deep reinforcement learning toolkit from Tianjin University’s Deep Reinforcement Learning Lab (TJU-DRL-LAB). Billed as a next-generation DRL toolkit, it packages a broad collection of reinforcement learning algorithms and training utilities into one framework so researchers can run, compare and extend RL methods without re-implementing them from scratch.
AI-Optimizer is an open-source project on GitHub with about 3,500 stars, last updated in 2023. It covers the deep reinforcement learning workflow end to end: environments, algorithm implementations, training loops and evaluation. The toolkit spans value-based and policy-based methods as well as topics such as transfer learning, reflecting the research focus of the lab behind it. Its modular design means individual algorithms can be studied, swapped or extended, which makes it useful both as a research platform and as a learning resource for students entering the RL field. Everything runs in Python on standard deep-learning stacks.
AI-Optimizer is completely free and open source. Download it from GitHub and run it on your own hardware โ there are no license fees, tiers or usage limits.
The project comes out of an active university research lab, so the implementations track the methods the group publishes on, and the code is organised to be read as much as run โ a practical way to see how modern DRL algorithms are actually put together inside a shared training framework.
AI-Optimizer is for reinforcement learning researchers, graduate students and engineers who want a single, hackable codebase containing many DRL algorithms for experiments, benchmarking or coursework.