activity
20182022
most citedSMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving

103 citations · 321 across the 26 of their papers we have counts for

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7 papers · 1 filter

cs.AI2021

Measuring the Non-Transitivity in Chess

Ricky Sanjaya, Jun Wang, Yaodong Yang

It has long been believed that Chess is the \emph{Drosophila} of Artificial Intelligence (AI). Studying Chess can productively provide valid knowledge about complex systems. Althou…

cs.AI202110 cited

Cooperative Multi-Agent Transfer Learning with Level-Adaptive Credit Assignment

Tianze Zhou, Fubiao Zhang, Kun Shao +10

Extending transfer learning to cooperative multi-agent reinforcement learning (MARL) has recently received much attention. In contrast to the single-agent setting, the coordination…

cs.AI2021

Neural Auto-Curricula

Xidong Feng, Oliver Slumbers, Ziyu Wan +5

When solving two-player zero-sum games, multi-agent reinforcement learning (MARL) algorithms often create populations of agents where, at each iteration, a new agent is discovered…

cs.AI2021

Modelling Behavioural Diversity for Learning in Open-Ended Games

Nicolas Perez Nieves, Yaodong Yang, Oliver Slumbers +3

Promoting behavioural diversity is critical for solving games with non-transitive dynamics where strategic cycles exist, and there is no consistent winner (e.g., Rock-Paper-Scissor…

cs.AI202110 cited

Diverse Auto-Curriculum is Critical for Successful Real-World Multiagent Learning Systems

Yaodong Yang, Jun Luo, Ying Wen +5

Multiagent reinforcement learning (MARL) has achieved a remarkable amount of success in solving various types of video games. A cornerstone of this success is the auto-curriculum f…

cs.AI20201 cited

Learning to Infer User Hidden States for Online Sequential Advertising

Zhaoqing Peng, Junqi Jin, Lan Luo +11

To drive purchase in online advertising, it is of the advertiser's great interest to optimize the sequential advertising strategy whose performance and interpretability are both im…