54 citations · 59 across the 9 of their papers we have counts for
11 papers
Escaping Local Views: Discovering Latent Concepts for Interpretable Multi-Agent Reinforcement Learning
Yijie Sun, Sanquan Sun, Yanda Zhu +3
Efficient cooperation is challenging due to the usual partial observability of each agent in multi-agent reinforcement learning. Recurrent networks encode local interaction histori…
Conditional Diffusion Model for Multi-Agent Dynamic Task Decomposition
Yanda Zhu, Yuanyang Zhu, Daoyi Dong +2
Task decomposition has shown promise in complex cooperative multi-agent reinforcement learning (MARL) tasks, which enables efficient hierarchical learning for long-horizon tasks in…
High-order Interactions Modeling for Interpretable Multi-Agent Q-Learning
Qinyu Xu, Yuanyang Zhu, Xuefei Wu +1
The ability to model interactions among agents is crucial for effective coordination and understanding their cooperation mechanisms in multi-agent reinforcement learning (MARL). Ho…
Concept Learning for Cooperative Multi-Agent Reinforcement Learning
Zhonghan Ge, Yuanyang Zhu, Chunlin Chen
Despite substantial progress in applying neural networks (NN) to multi-agent reinforcement learning (MARL) areas, they still largely suffer from a lack of transparency and interope…
Learning Individual Intrinsic Reward in Multi-Agent Reinforcement Learning via Incorporating Generalized Human Expertise
Xuefei Wu, Xiao Yin, Yuanyang Zhu +1
Efficient exploration in multi-agent reinforcement learning (MARL) is a challenging problem when receiving only a team reward, especially in environments with sparse rewards. A pow…
Discretizing Continuous Action Space with Unimodal Probability Distributions for On-Policy Reinforcement Learning
Yuanyang Zhu, Zhi Wang, Yuanheng Zhu +2
For on-policy reinforcement learning, discretizing action space for continuous control can easily express multiple modes and is straightforward to optimize. However, without consid…
BiERL: A Meta Evolutionary Reinforcement Learning Framework via Bilevel Optimization
Junyi Wang, Yuanyang Zhu, Zhi Wang +3
Evolutionary reinforcement learning (ERL) algorithms recently raise attention in tackling complex reinforcement learning (RL) problems due to high parallelism, while they are prone…
Boosting Value Decomposition via Unit-Wise Attentive State Representation for Cooperative Multi-Agent Reinforcement Learning
Qingpeng Zhao, Yuanyang Zhu, Zichuan Liu +2
In cooperative multi-agent reinforcement learning (MARL), the environmental stochasticity and uncertainties will increase exponentially when the number of agents increases, which p…
NQ: Neural Attention Additive Model for Interpretable Multi-Agent Q-Learning
Zichuan Liu, Yuanyang Zhu, Chunlin Chen
Value decomposition is widely used in cooperative multi-agent reinforcement learning, however, its implicit credit assignment mechanism is not yet fully understood due to black-box…
MIXRTs: Toward Interpretable Multi-Agent Reinforcement Learning via Mixing Recurrent Soft Decision Trees
Zichuan Liu, Yuanyang Zhu, Zhi Wang +2
While achieving tremendous success in various fields, existing multi-agent reinforcement learning (MARL) with a black-box neural network makes decisions in an opaque manner that hi…
Rule-Based Reinforcement Learning for Efficient Robot Navigation with Space Reduction
Yuanyang Zhu, Zhi Wang, Chunlin Chen +1
For real-world deployments, it is critical to allow robots to navigate in complex environments autonomously. Traditional methods usually maintain an internal map of the environment…