Results 31 to 40 of about 137,924 (252)
Multi-Agent Deep Reinforcement Learning with Human Strategies
, 2019 Deep learning has enabled traditional reinforcement learning methods to deal with high-dimensional problems. However, one of the disadvantages of deep reinforcement learning methods is the limited exploration capacity of learning agents.
Nahavandi, Saeid +2 more
core +1 more source
Low-Cost Multi-Agent Navigation via Reinforcement Learning With Multi-Fidelity Simulator
IEEE Access, 2021 In recent years, reinforcement learning (RL) has been widely used to solve multi-agent navigation tasks, and a high-fidelity level for the simulator is critical to narrow the gap between simulation and real-world tasks.
Jiantao Qiu +6 more
doaj +1 more source
IET Smart Grid, 2022 Residential buildings are large consumers of energy. They contribute significantly to the demand placed on the grid, particularly during hours of peak demand.
Junlin Lu, Patrick Mannion, Karl Mason
doaj +1 more source
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
Molecular Oncology, EarlyView.The cancer problem is increasing globally with projections up to the year 2050 showing unfavourable outcomes in terms of incidence and cancer‐related deaths. The main challenges are prevention, improved therapeutics resulting in increased cure rates and enhanced health‐related quality of life.
Ulrik Ringborg +43 more
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
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
Measuring collaborative emergent behavior in multi-agent reinforcement learning
, 2018 Multi-agent reinforcement learning (RL) has important implications for the future of human-agent teaming. We show that improved performance with multi-agent RL is not a guarantee of the collaborative behavior thought to be important for solving multi ...
E Rovira +4 more
core +1 more source
Multi-Agent Common Knowledge Reinforcement Learning
, 2018 Advances in Neural Information Processing Systems, 9924 ...
de Witt, C +5 more
openaire +3 more sources
Overview of molecular signatures of senescence and associated resources: pros and cons
FEBS Open Bio, EarlyView.Cells can enter a stress response state termed cellular senescence that is involved in various diseases and aging. Detecting these cells is challenging due to the lack of universal biomarkers. This review presents the current state of senescence identification, from biomarkers to molecular signatures, compares tools and approaches, and highlights ...
Orestis A. Ntintas +6 more
wiley +1 more source