34 citations · 39 across the 9 of their papers we have counts for
4 papers · 1 filter
Safety Training Modulates Harmful Misalignment Under On-Policy RL, But Direction Depends on Environment Design
Leon Eshuijs, Shihan Wang, Antske Fokkens
Specification gaming under Reinforcement Learning (RL) is known to cause LLMs to develop sycophantic, manipulative, or deceptive behavior, yet the conditions under which this occur…
Credit Assignment and Efficient Exploration based on Influence Scope in Multi-agent Reinforcement Learning
Shuai Han, Mehdi Dastani, Shihan Wang
Training cooperative agents in sparse-reward scenarios poses significant challenges for multi-agent reinforcement learning (MARL). Without clear feedback on actions at each step in…
Reducing Variance Caused by Communication in Decentralized Multi-agent Deep Reinforcement Learning
Changxi Zhu, Mehdi Dastani, Shihan Wang
In decentralized multi-agent deep reinforcement learning (MADRL), communication can help agents to gain a better understanding of the environment to better coordinate their behavio…
Sample Efficient Reinforcement Learning by Automatically Learning to Compose Subtasks
Shuai Han, Mehdi Dastani, Shihan Wang
Improving sample efficiency is central to Reinforcement Learning (RL), especially in environments where the rewards are sparse. Some recent approaches have proposed to specify rewa…