4 papers
Task diversity produces systematic transfer but inhibits continual reinforcement learning
Purab Seth, Neil Shah, Kunal Jha +3
Continual reinforcement learning aims to produce agents that learn not only to improve at their current tasks but also to adapt as task distributions change. Training an agent on m…
Modeling Others' Minds as Code
Kunal Jha, Aydan Yuenan Huang, Eric Ye +2
Accurate prediction of human behavior is essential for robust and safe human-AI collaboration. However, existing approaches for modeling people are often data-hungry and brittle be…
NiceWebRL: a Python library for human subject experiments with reinforcement learning environments
Wilka Carvalho, Vikram Goddla, Ishaan Sinha +2
We present NiceWebRL, a research tool that enables researchers to use machine reinforcement learning (RL) environments for online human subject experiments. NiceWebRL is a Python l…
Cross-environment Cooperation Enables Zero-shot Multi-agent Coordination
Kunal Jha, Wilka Carvalho, Yancheng Liang +3
Zero-shot coordination (ZSC), the ability to adapt to a new partner in a cooperative task, is a critical component of human-compatible AI. While prior work has focused on training…