Abstract: Continuous-time reinforcement learning (CT-RL) methods hold great promise in real-world applications. Adaptive dynamic programming (ADP)-based CT-RL algorithms, especially their theoretical ...
Overview: Reinforcement learning in 2025 is more practical than ever, with Python libraries evolving to support real-world simulations, robotics, and deci ...
Abstract: Despite the significant advancements in single-agent evolutionary reinforcement learning, research exploring evolutionary reinforcement learning within multi-agent systems is still in its ...
AI agents are reshaping software development, from writing code to carrying out complex instructions. Yet LLM-based agents are prone to errors and often perform poorly on complicated, multi-step tasks ...
Fine-tuning & Reinforcement Learning for LLMs. 🦥 Train OpenAI gpt-oss, DeepSeek-R1, Qwen3, Gemma 3, TTS 2x faster with 70% less VRAM.
RLinf is a flexible and scalable open-source RL infrastructure designed for Embodied and Agentic AI. The 'inf' in RLinf stands for Infrastructure, highlighting its role as a robust backbone for ...
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