These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
232 related articles for article (PubMed ID: 25181517)
1. Accelerating Multiagent Reinforcement Learning by Equilibrium Transfer. Hu Y; Gao Y; An B IEEE Trans Cybern; 2015 Jul; 45(7):1289-302. PubMed ID: 25181517 [TBL] [Abstract][Full Text] [Related]
2. Multiagent reinforcement learning with unshared value functions. Hu Y; Gao Y; An B IEEE Trans Cybern; 2015 Apr; 45(4):647-62. PubMed ID: 25014990 [TBL] [Abstract][Full Text] [Related]
3. Multiagent Reinforcement Learning With Sparse Interactions by Negotiation and Knowledge Transfer. Zhou L; Yang P; Chen C; Gao Y IEEE Trans Cybern; 2017 May; 47(5):1238-1250. PubMed ID: 27046917 [TBL] [Abstract][Full Text] [Related]
4. Lateral Transfer Learning for Multiagent Reinforcement Learning. Shi H; Li J; Mao J; Hwang KS IEEE Trans Cybern; 2023 Mar; 53(3):1699-1711. PubMed ID: 34506297 [TBL] [Abstract][Full Text] [Related]
5. Expected Policy Gradient for Network Aggregative Markov Games in Continuous Space. Moghaddam AR; Kebriaei H IEEE Trans Neural Netw Learn Syst; 2024 Apr; PP():. PubMed ID: 38648129 [TBL] [Abstract][Full Text] [Related]
6. Approximating Nash equilibrium for anti-UAV jamming Markov game using a novel event-triggered multi-agent reinforcement learning. Feng Z; Huang M; Wu Y; Wu D; Cao J; Korovin I; Gorbachev S; Gorbacheva N Neural Netw; 2023 Apr; 161():330-342. PubMed ID: 36774870 [TBL] [Abstract][Full Text] [Related]
7. From Nash Equilibria to Chain Recurrent Sets: An Algorithmic Solution Concept for Game Theory. Papadimitriou C; Piliouras G Entropy (Basel); 2018 Oct; 20(10):. PubMed ID: 33265870 [TBL] [Abstract][Full Text] [Related]
8. Transfer of conflict and cooperation from experienced games to new games: a connectionist model of learning. Spiliopoulos L Front Neurosci; 2015; 9():102. PubMed ID: 25873855 [TBL] [Abstract][Full Text] [Related]
10. SATF: A Scalable Attentive Transfer Framework for Efficient Multiagent Reinforcement Learning. Chen B; Cao Z; Bai Q IEEE Trans Neural Netw Learn Syst; 2024 Apr; PP():. PubMed ID: 38648131 [TBL] [Abstract][Full Text] [Related]
11. A Collaborative Multiagent Reinforcement Learning Method Based on Policy Gradient Potential. Zhang Z; Ong YS; Wang D; Xue B IEEE Trans Cybern; 2021 Feb; 51(2):1015-1027. PubMed ID: 31443061 [TBL] [Abstract][Full Text] [Related]
12. Decentralized learning in Markov games. Vrancx P; Verbeeck K; Nowé A IEEE Trans Syst Man Cybern B Cybern; 2008 Aug; 38(4):976-81. PubMed ID: 18632387 [TBL] [Abstract][Full Text] [Related]
13. An impossibility theorem in game dynamics. Milionis J; Papadimitriou C; Piliouras G; Spendlove K Proc Natl Acad Sci U S A; 2023 Oct; 120(41):e2305349120. PubMed ID: 37796988 [TBL] [Abstract][Full Text] [Related]
14. Strangeness-driven exploration in multi-agent reinforcement learning. Kim JB; Choi HB; Han YH Neural Netw; 2024 Apr; 172():106149. PubMed ID: 38306786 [TBL] [Abstract][Full Text] [Related]
15. Multiagent Adversarial Collaborative Learning via Mean-Field Theory. Luo G; Zhang H; He H; Li J; Wang FY IEEE Trans Cybern; 2021 Oct; 51(10):4994-5007. PubMed ID: 33095725 [TBL] [Abstract][Full Text] [Related]
16. Adaptive Individual Q-Learning-A Multiagent Reinforcement Learning Method for Coordination Optimization. Zhang Z; Wang D IEEE Trans Neural Netw Learn Syst; 2024 Apr; PP():. PubMed ID: 38625776 [TBL] [Abstract][Full Text] [Related]
17. On Nash Equilibrium and Evolutionarily Stable States That Are Not Characterised by the Folk Theorem. Li J; Kendall G PLoS One; 2015; 10(8):e0136032. PubMed ID: 26288088 [TBL] [Abstract][Full Text] [Related]
18. Dynamical selection of Nash equilibria using reinforcement learning: Emergence of heterogeneous mixed equilibria. Nicole R; Sollich P PLoS One; 2018; 13(7):e0196577. PubMed ID: 29985923 [TBL] [Abstract][Full Text] [Related]
19. Learning Automata-Based Multiagent Reinforcement Learning for Optimization of Cooperative Tasks. Zhang Z; Wang D; Gao J IEEE Trans Neural Netw Learn Syst; 2021 Oct; 32(10):4639-4652. PubMed ID: 33027003 [TBL] [Abstract][Full Text] [Related]
20. KnowRU: Knowledge Reuse via Knowledge Distillation in Multi-Agent Reinforcement Learning. Gao Z; Xu K; Ding B; Wang H Entropy (Basel); 2021 Aug; 23(8):. PubMed ID: 34441184 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]