摇曳的微光 发表于 2025-3-26 21:02:21
Context-Aware Multi-agent Coordination with Loose Couplings and Repeated Interaction,ming technique to improve the context exploitation process and a variable elimination technique to efficiently perform the maximization through exploiting the loose couplings. Third, two enhancements to MACUCB are proposed with improved theoretical guarantees. Fourth, we derive theoretical bounds on无情 发表于 2025-3-27 02:32:29
http://reply.papertrans.cn/29/2818/281740/281740_32.png抱怨 发表于 2025-3-27 07:23:21
978-3-030-64095-8Springer Nature Switzerland AG 2020衍生 发表于 2025-3-27 10:17:25
http://reply.papertrans.cn/29/2818/281740/281740_34.png乳白光 发表于 2025-3-27 16:24:13
http://reply.papertrans.cn/29/2818/281740/281740_35.pnginchoate 发表于 2025-3-27 19:42:11
https://doi.org/10.1007/978-3-319-24237-8 space. Such algorithms work well in tasks with relatively slight differences. However, when the task distribution becomes wider, it would be quite inefficient to directly learn such a meta-policy. In this paper, we propose a new meta-RL algorithm called Meta Goal-generation for Hierarchical RL (MGH果仁 发表于 2025-3-27 23:33:59
Alaska-Siberian Air Road, “ALSIB”gh dimensional robotic control problems. In this regard, we propose the D3PG approach, which is a multiagent extension of DDPG by decomposing the global critic into a weighted sum of local critics. Each of these critics is modeled as an individual learning agent that governs the decision making of aTincture 发表于 2025-3-28 03:50:03
The Eastern Arctic Seas Encyclopediaagent control, systems are complex with unknown or highly uncertain dynamics, where traditional model-based control methods can hardly be applied. Compared with model-based control in control theory, deep reinforcement learning (DRL) is promising to learn the controller/policy from data without the上下倒置 发表于 2025-3-28 08:41:47
Finding a Way Forward for Free Tradeization. An independent learner may receive different rewards for the same state and action at different time steps, depending on the actions of the other agents in that state. Existing multi-agent learning methods try to overcome these issues by using various techniques, such as hysteresis or leniegain631 发表于 2025-3-28 13:49:20
Education, Talent, and Cultural Tiesis issue include the intrinsically motivated goal exploration processes (IMGEP) and the maximum state entropy exploration (MSEE). In this paper, we propose a goal-selection criterion in IMGEP based on the principle of MSEE, which results in the new exploration method .. Novelty-pursuit performs the