177 citations · 995 across the 65 of their papers we have counts for
10 papers · 1 filter
SCC-rFMQ Learning in Cooperative Markov Games with Continuous Actions
Chengwei Zhang, Xiaohong Li, Jianye Hao +5
Although many reinforcement learning methods have been proposed for learning the optimal solutions in single-agent continuous-action domains, multiagent coordination domains with c…
Hierarchical Deep Multiagent Reinforcement Learning with Temporal Abstraction
Hongyao Tang, Jianye Hao, Tangjie Lv +8
Multiagent reinforcement learning (MARL) is commonly considered to suffer from non-stationary environments and exponentially increasing policy space. It would be even more challeng…
Towards Efficient Detection and Optimal Response against Sophisticated Opponents
Tianpei Yang, Zhaopeng Meng, Jianye Hao +3
Multiagent algorithms often aim to accurately predict the behaviors of other agents and find a best response accordingly. Previous works usually assume an opponent uses a stationar…
Learning Adaptive Display Exposure for Real-Time Advertising
Weixun Wang, Junqi Jin, Jianye Hao +9
In E-commerce advertising, where product recommendations and product ads are presented to users simultaneously, the traditional setting is to display ads at fixed positions. Howeve…
An Optimal Rewiring Strategy for Reinforcement Social Learning in Cooperative Multiagent Systems
Hongyao Tang, Li Wang, Zan Wang +2
Multiagent coordination in cooperative multiagent systems (MASs) has been widely studied in both fixed-agent repeated interaction setting and the static social learning framework.…
Falsification of Cyber-Physical Systems Using Deep Reinforcement Learning
Takumi Akazaki, Shuang Liu, Yoriyuki Yamagata +2
With the rapid development of software and distributed computing, Cyber-Physical Systems (CPS) are widely adopted in many application areas, e.g., smart grid, autonomous automobile…
Hierarchical Heuristic Learning towards Effcient Norm Emergence
Tianpei Yang, Jianye Hao, Zhaopeng Meng +2
Social norms serve as an important mechanism to regulate the behaviors of agents and to facilitate coordination among them in multiagent systems. One important research question is…
SA-IGA: A Multiagent Reinforcement Learning Method Towards Socially Optimal Outcomes
Chengwei Zhang, Xiaohong Li, Jianye Hao +3
In multiagent environments, the capability of learning is important for an agent to behave appropriately in face of unknown opponents and dynamic environment. From the system desig…
Towards Cooperation in Sequential Prisoner's Dilemmas: a Deep Multiagent Reinforcement Learning Approach
Weixun Wang, Jianye Hao, Yixi Wang +1
The Iterated Prisoner's Dilemma has guided research on social dilemmas for decades. However, it distinguishes between only two atomic actions: cooperate and defect. In real-world p…
Weighted Double Deep Multiagent Reinforcement Learning in Stochastic Cooperative Environments
Yan Zheng, Jianye Hao, Zongzhang Zhang
Recently, multiagent deep reinforcement learning (DRL) has received increasingly wide attention. Existing multiagent DRL algorithms are inefficient when facing with the non-station…