Results 41 to 50 of about 139,994 (273)
Arena: A General Evaluation Platform and Building Toolkit for Multi-Agent Intelligence
, 2019 Learning agents that are not only capable of taking tests, but also innovating is becoming a hot topic in AI. One of the most promising paths towards this vision is multi-agent learning, where agents act as the environment for each other, and improving ...
Aryan, Abi +8 more
core +1 more source
Curriculum Learning for Cooperation in Multi-Agent Reinforcement Learning
CoRR, 2023 While there has been significant progress in curriculum learning and continuous learning for training agents to generalize across a wide variety of environments in the context of single-agent reinforcement learning, it is unclear if these algorithms would still be valid in a multi-agent setting. In a competitive setting, a learning agent can be trained
Rupali Bhati +3 more
openaire +2 more sources
Digital twins to accelerate target identification and drug development for immune‐mediated disorders
FEBS Open Bio, EarlyView.Digital twins integrate patient‐derived molecular and clinical data into personalised computational models that simulate disease mechanisms. They enable rapid identification and validation of therapeutic targets, prediction of drug responses, and prioritisation of candidate interventions.
Anna Niarakis, Philippe Moingeon
wiley +1 more source
Reducing Q-Value Estimation Bias via Mutual Estimation and Softmax Operation in MADRL
AlgorithmsWith the development of electronic game technology, the content of electronic games presents a larger number of units, richer unit attributes, more complex game mechanisms, and more diverse team strategies.
Zheng Li +4 more
doaj +1 more source
Multi-agent reinforcement learning for route guidance system [PDF]
, 2011 Nowadays, multi-agent systems are used to create applications in a variety of areas, including economics, management, transportation, telecommunications, etc. Importantly, in many domains, the reinforcement learning agents try to learn a task by directly
Arokhlo, Mortaza Zolfpour +3 more
core
Multi-Agent Common Knowledge Reinforcement Learning
CoRR, 2018 Advances in Neural Information Processing Systems, 9924 ...
de Witt, C +5 more
openaire +4 more sources
Advanced Engineering Materials, EarlyView.Additive manufacturing provides precise control over the placement of continuous fibres within polymer matrices, enabling customised mechanical performance in composite components. This article explores processing strategies, mechanical testing, and modelling approaches for additive manufactured continuous fibre‐reinforced composites.
Cherian Thomas, Amir Hosein Sakhaei
wiley +1 more source
Design of Multi-Agent Angle Tracking Method Based on Deep Reinforcement Learning [PDF]
Jisuanji gongchengIn intelligent situational awareness application scenarios, multi-agent angle tracking problems often occur when moving targets must be monitored and controlled.
BI Qian, QIAN Cheng, ZHANG Ke, WANG Cheng
doaj +1 more source
Learning to Communicate with Deep Multi-Agent Reinforcement Learning
CoRR, 2016 We consider the problem of multiple agents sensing and acting in environments with the goal of maximising their shared utility. In these environments, agents must learn communication protocols in order to share information that is needed to solve the tasks.
Foerster, J +3 more
openaire +4 more sources
Concept Learning for Interpretable Multi-Agent Reinforcement Learning
CoRR, 2023 Multi-agent robotic systems are increasingly operating in real-world environments in close proximity to humans, yet are largely controlled by policy models with inscrutable deep neural network representations. We introduce a method for incorporating interpretable concepts from a domain expert into models trained through multi-agent reinforcement ...
Renos Zabounidis +4 more
openaire +3 more sources