IEEE

Reinforcement Learning for Multi-Agent Path Finding in Large-Scale Warehouses via Distributed Policy Evolution

Abstract: Efficient multi-agent path finding (MAPF) is essential for large-scale warehousing and logistics systems. Despite the potential of reinforcement learning (RL) methods, current approaches ...
IEEE

Curricular Subgoals for Inverse Reinforcement Learning

Abstract: Inverse Reinforcement Learning (IRL) aims to reconstruct the reward function from expert demonstrations to facilitate policy learning, and has demonstrated its remarkable success in ...
Deep Learning with Yacine on MSN

What are RLVR environments for LLMs? | Policy, rollouts & rubrics explained

A clear breakdown of RLVR environments for LLMs — what they are, how policies and rollouts work, and the role of rubrics in the process. Perfect for anyone interested in reinforcement learning and AI ...
GitHub

Demystifying Reinforcement Learning in Agentic Reasoning

An overview of our research on agentic RL. In this work, we systematically investigate three dimensions of agentic RL: data, algorithms, and reasoning modes. Our findings reveal: Real end-to-end ...
Hosted on MSN

Unique friendships: The good student and the rebel

Dive into the unexpected world of school dynamics with our hilarious take on the good student vs. bad student scenario! Watch as a diligent boy navigates the complexities of popularity while a fierce ...
techxplore

Reinforcement learning accelerates model-free training of optical AI systems

Optical computing has emerged as a powerful approach for high-speed and energy-efficient information processing. Diffractive optical networks, in particular, enable large-scale parallel computation ...