4 citations · 6 across the 5 of their papers we have counts for
5 papers
Task diversity produces systematic transfer but inhibits continual reinforcement learning
Purab Seth, Neil Shah, Ishaan Sinha +4
Continual reinforcement learning (RL) aims to produce agents that never stop adapting to new tasks. A key question is how this interacts with the diversity of tasks an agent experi…
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…
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…
Predictive representations: building blocks of intelligence
Wilka Carvalho, Momchil S. Tomov, William de Cothi +2
Adaptive behavior often requires predicting future events. The theory of reinforcement learning prescribes what kinds of predictive representations are useful and how to compute th…