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 ...
Artificial Intelligence (AI) has achieved remarkable successes in recent years. It can defeat human champions in games like Go, predict protein structures with high accuracy, and perform complex tasks ...
Overview: Reinforcement learning in 2025 is more practical than ever, with Python libraries evolving to support real-world simulations, robotics, and deci ...
So far, running LLMs has required a large amount of computing resources, mainly GPUs. Running locally, a simple prompt with a typical LLM takes on an average Mac ...
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.
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