Aerospace and Mechanical Insider on MSN

Hierarchical reinforcement learning boosts air defense efficiency

Modern air defense confrontations demand rapid, precise task assignments in environments where threats evolve within seconds.

经典之作PPO算法：曾被NeurIPS拒了

PPO（Proximal Policy Optimization）这个后来在 RLHF 和大模型训练中被广泛使用的经典算法，当年曾被 NIPS 2017 拒之门外。 这件事最近由 PPO 作者 John Schulman 本人提起。他只用一句话概括了这段往事：PPO，曾经被 NIPS 2017 拒了。 这篇最早在 2017 年 7 月发布的论文，当时看起来只是一个更简单、更工程友好的策略优化算法。
IEEE

Cooperative Hunting of Unmanned Surface Vehicles via Multi-Agent Proximal Policy ...

Abstract: This paper proposes a cooperative hunting algorithm based on multi-agent reinforcement learning (MARL) to address the problem of cooperative hunting at sea involving an evasive target. First ...
IEEE

Proximal Policy Optimization With Advantage Reuse Competition

Abstract: In recent years, reinforcement learning (RL) has made great achievements in artificial intelligence. Proximal policy optimization (PPO) is a representative RL algorithm, which limits the ...