9 citations · 12 across the 7 of their papers we have counts for
8 papers
Prospective Interpretation Risk: Principled Communication Control Between LLMs
Wanrong Yang, Rehan Deen, Julian Ma +7
Large language model (LLM) agentic systems increasingly rely on models communicating with one another, yet existing uncertainty and multi-agent methods rarely estimate how a partic…
MANSA: Learning Fast and Slow in Multi-Agent Systems
David Mguni, Haojun Chen, Taher Jafferjee +7
In multi-agent reinforcement learning (MARL), independent learning (IL) often shows remarkable performance and easily scales with the number of agents. Yet, using IL can be ineffic…
Taming Multi-Agent Reinforcement Learning with Estimator Variance Reduction
Taher Jafferjee, Juliusz Ziomek, Tianpei Yang +6
Centralised training with decentralised execution (CT-DE) serves as the foundation of many leading multi-agent reinforcement learning (MARL) algorithms. Despite its popularity, it…
Reinforcement Learning in Presence of Discrete Markovian Context Evolution
Hang Ren, Aivar Sootla, Taher Jafferjee +3
We consider a context-dependent Reinforcement Learning (RL) setting, which is characterized by: a) an unknown finite number of not directly observable contexts; b) abrupt (disconti…
Saute RL: Almost Surely Safe Reinforcement Learning Using State Augmentation
Aivar Sootla, Alexander I. Cowen-Rivers, Taher Jafferjee +4
Satisfying safety constraints almost surely (or with probability one) can be critical for the deployment of Reinforcement Learning (RL) in real-life applications. For example, plan…
LIGS: Learnable Intrinsic-Reward Generation Selection for Multi-Agent Learning
David Henry Mguni, Taher Jafferjee, Jianhong Wang +7
Efficient exploration is important for reinforcement learners to achieve high rewards. In multi-agent systems, coordinated exploration and behaviour is critical for agents to joint…
Learning to Shape Rewards using a Game of Two Partners
David Mguni, Taher Jafferjee, Jianhong Wang +9
Reward shaping (RS) is a powerful method in reinforcement learning (RL) for overcoming the problem of sparse or uninformative rewards. However, RS typically relies on manually engi…
Mitigating Value Hallucination in Dyna Planning via Multistep Predecessor Models
Farzane Aminmansour, Taher Jafferjee, Ehsan Imani +3
Dyna-style reinforcement learning (RL) agents improve sample efficiency over model-free RL agents by updating the value function with simulated experience generated by an environme…