Agents
Grounded Answers for Multi-agent Decision-making Problem through Generative World Model
Figure 1: Complex decision problems that require a good understanding of the environment's
Aligning Individual and Collective Objectives in Multi-Agent Cooperation
Among the research topics in multi-agent learning, mixed-motive cooperation is one of the most prominent challenges, primarily due to the mismatch between individual and collective goals.
Constrained Human-AI Cooperation: An Inclusive Embodied Social Intelligence Challenge
Left: A human partner is confined to a wheelchair and struggles to move past an obstacle.
Boosting Sample Efficiency and Generalization in Multi-agent Reinforcement Learning via Equivariance
These challenges are partially due to a lack of structure or inductive bias in the neural networks typically used in learning the policy. One such form of structure that is commonly observed in multi-agent scenarios is symmetry.
Maximizing utility in multi-agent environments by anticipating the behavior of other learners
With the increased use of learning algorithms as a means to optimize agents' objectives in unknown
Bias Amplification in Language Model Evolution: An Iterated Learning Perspective
We draw parallels between the behavior of LLMs and the evolution of human culture, as the latter has been extensively studied by cognitive scientists for decades.