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 ...
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 ...
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Hierarchical reinforcement learning boosts air defense efficiency
Modern air defense confrontations demand rapid, precise task assignments in environments where threats evolve within seconds.
We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors ...
连续动作空间的 PPO 算法实现 多智能体环境支持 10 种训练技巧优化 TensorBoard 训练可视化 自定义 MPE 环境（多无人机 ...
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。
The proliferation of AI-enabled personal computers with heterogeneous processing units (CPU, GPU, NPU) introduces substantial complexity into resource scheduling due to dynamic neural network ...
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