ASSUME Framework
✨An open-source agent-based simulation toolbox for European electricity markets, supporting deep reinforcement learning bidding strategies and grid congestion management modeling.
An open-source agent-based simulation toolbox for European electricity markets, supporting deep reinforcement learning bidding strategies and grid congestion management modeling.
An open-source framework for building, evaluating, and training general multi-agent systems. Features natural language agent creation, distributed reinforcement learning training pipeline, and complex environment interactions. Ranks top on authoritative benchmarks including GAIA, OSWorld, and VisualWebArena.
A generative agent framework inspired by human dual-process theory, combining fast and slow thinking mechanisms with in-context reinforcement learning to efficiently solve complex interactive reasoning tasks.
An LLM post-training framework for RL scaling by Tsinghua THUDM, deeply integrating Megatron-LM training with SGLang inference engine for distributed reinforcement learning on large models like GLM, Qwen, DeepSeek, and Llama.
AI-Compass is a comprehensive open-source project that provides learning paths and practical guidelines for AI technologies, helping users from beginners to professionals build a complete AI knowledge system from fundamental theory to cutting-edge applications.
A curated collection of autonomous agents (LLM) research papers updated daily, providing the latest AI research findings for researchers and developers。
Odyssey is a framework that empowers LLM-based Minecraft agents with open-world skills, featuring 40 primitive skills and 183 compositional skills, enabling AI to autonomously explore, learn, and execute diverse tasks in the Minecraft universe.
A curated collection of recent research papers on autonomous agents, focusing on both reinforcement learning-based and large language model-based approaches, helping researchers quickly understand the cutting edge of the field。
A Python tutorial repository providing runnable examples and theoretical explanations for deep reinforcement learning, classical reinforcement learning, and machine learning concepts.
Trinity-RFT is a general-purpose, flexible and user-friendly framework for LLM reinforcement fine-tuning (RFT). It decouples RFT into three coordinated components: Explorer, Trainer, and Buffer, enabling users with different backgrounds to train LLM-powered agents for specific domains.
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