Abstract: This paper introduces novel Bellman mappings (BMaps) for value iteration (VI) in distributed reinforcement learning (DRL), where agents are deployed over an undirected, connected ...
Abstract: This paper develops a distributed reinforcement learning (RL) method to coordinate cooperative microgrids (MGs). The high uncertainty of power loads and renewable energy sources motivate the ...
How do you keep reinforcement learning for large reasoning models from stalling on a few very long, very slow rollouts while GPUs sit under used? a team of researchers from Moonshot AI and Tsinghua ...
Researchers at Google Cloud and UCLA have proposed a new reinforcement learning framework that significantly improves the ability of language models to learn very challenging multi-step reasoning ...
Effective task allocation has become a critical challenge for multi-robot systems operating in dynamic environments like search and rescue. Traditional methods, often based on static data and ...
(A) Internet of Medical Things (IoMT) devices collect medical data then encrypt it and sent to a blockchain for secure storage. (B) Reinforcement learning (RL) agents monitor activity to detect ...
Download PDF Join the Discussion View in the ACM Digital Library Deep reinforcement learning (DRL) has elevated RL to complex environments by employing neural network representations of policies. 1 It ...
David Shan is the Co-Founder and CTO of Clado, who trains in-house small language models to build the best people search algorithm. We celebrate RL breakthroughs, but behind the hype lies a brittle ...
“Trauma-informed” teaching is a popular phrase in education circles, but what does it actually look like on the ground? Today, some educators share a practical perspective. Marie Moreno, Ed.D., is an ...
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