5 papers
Predictive Auxiliary Learning for Belief-based Multi-Agent Systems
Qinwei Huang, Stefan Wang, Simon Khan +2
The performance of multi-agent reinforcement learning (MARL) in partially observable environments depends on effectively aggregating information from observations, communications,…
Linearithmic Clean-up for Vector-Symbolic Key-Value Memory with Kroneker Rotation Products
Ruipeng Liu, Qinru Qiu, Simon Khan +1
A computational bottleneck in current Vector-Symbolic Architectures (VSAs) is the ``clean-up'' step, which decodes the noisy vectors retrieved from the architecture. Clean-up typic…
EMAC+: Embodied Multimodal Agent for Collaborative Planning with VLM+LLM
Shuang Ao, Flora D. Salim, Simon Khan
Although LLMs demonstrate proficiency in several text-based reasoning and planning tasks, their implementation in robotics control is constrained by significant deficiencies: (1) L…
Near-Optimal Sample Complexity for Iterated CVaR Reinforcement Learning with a Generative Model
Zilong Deng, Simon Khan, Shaofeng Zou
In this work, we study the sample complexity problem of risk-sensitive Reinforcement Learning (RL) with a generative model, where we aim to maximize the Conditional Value at Risk (…
Why the Agent Made that Decision: Contrastive Explanation Learning for Reinforcement Learning
Rui Zuo, Simon Khan, Zifan Wang +2
Reinforcement learning (RL) has demonstrated remarkable success in solving complex decision-making problems, yet its adoption in critical domains is hindered by the lack of interpr…