TII's Autonomous Robotics Research Center (ARRC) and NYU's Agile Robotics and Perception Lab have released RLtools, an open-source reinforcement learning library. RLtools achieves a 75x speed-up in training compared to existing libraries, enabling drone controller training on standard computers. It allows training on consumer-grade laptops or directly on microcontrollers, addressing resource efficiency and deployment challenges. Why it matters: This library accelerates the development and deployment of autonomous systems by reducing training time and resource requirements, making advanced AI more accessible.
Researchers introduce MATRIX, a vision-centric agent tuning framework for robust tool-use reasoning in VLMs. The framework includes M-TRACE, a dataset of 28.5K multimodal tasks with 177K verified trajectories, and Pref-X, a set of 11K automatically generated preference pairs. Experiments show MATRIX consistently outperforms open- and closed-source VLMs across three benchmarks.
This paper presents a reinforcement learning framework for optimizing energy pricing in peer-to-peer (P2P) energy systems. The framework aims to maximize the profit of all components in a microgrid, including consumers, prosumers, the service provider, and a community battery. Experimental results on the Pymgrid dataset demonstrate the approach's effectiveness in price optimization, considering the interests of different components and the impact of community battery capacity.