Abstract: In response to the computing power processing needs of intelligent applications, Multi-access Edge Computing (MEC) provides computing services at the network edge. Unmanned Aerial Vehicles (UAVs) have good maneuverability and can serve as temporary aerial edge nodes in MEC to provide edge services to ground users. However, the MEC environment is complex and dynamically … Read more
1. Introduction Multi-Agent Reinforcement Learning (MARL) is an important branch of reinforcement learning that extends the traditional single-agent reinforcement learning concept to multi-agent environments. In MARL, multiple agents learn optimal strategies through interactions with the environment and other agents to maximize cumulative rewards in cooperative or competitive scenarios. Adversarial interactions among agents in MAgent (mixed … Read more
Deep Reinforcement Learning Laboratory Official Website：http://www.neurondance.com/Forum：http://deeprl.neurondance.com/This article is reproduced from: Machine Heart Researchers from Tsinghua University and UC Berkeley found that without any changes to the algorithm or network architecture, using MAPPO (Multi-Agent PPO) achieved performance comparable to SOTA algorithms on three representative multi-agent tasks (Multi-Agent Particle World, StarCraft II, Hanabi). In recent years, multi-agent … Read more
Click the “blue text” above, and select “Set as Favorite“ Key information, delivered on D1 time! Following the global success of DeepSeek, the deep reasoning and general agent frameworks represented by OpenAI Deep Research and Manus have once again ignited the tech community, showcasing remarkable intelligent reasoning and execution capabilities. However, in enterprise-level critical task … Read more