1 citations · 1 across the 2 of their papers we have counts for
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…
Naturalistic Computational Cognitive Science: Towards generalizable models and theories that capture the full range of natural behavior
Wilka Carvalho, Andrew Lampinen
How can cognitive science build generalizable theories that span the full scope of natural situations and behaviors? We argue that progress in Artificial Intelligence (AI) offers t…
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…