WooooDyy / AgentGym-RL

What it does

• Environment module. provides diverse scenarios via a standardized server–client architecture with unified HTTP protocols and parallel requests.
• Agent module. encapsulates the reasoning and decision-making process of agents in multi-turn interactions, with support for advanced mechanisms such as long-horizon planning and self-reflection.
• Training module. implements reinforcement learning pipelines and other training methods to optimize agent policies.
• Web Navigation. We include WebArena, a realistic and reproducible web environment containing 4 distinct domains prevalent on the internet: online shopping, discussioin forums, collaborative development, and bussiness contnt management.
• Deep Search. Building upon Search-R1, we include a RAG-based environment which enables LLMs to interact with search engines and solve multi-turn retrieval and reasoning tasks.