307 citations · 492 across the 33 of their papers we have counts for
4 papers · 1 filter
The Effective Horizon Explains Deep RL Performance in Stochastic Environments
Cassidy Laidlaw, Banghua Zhu, Stuart Russell +1
Reinforcement learning (RL) theory has largely focused on proving minimax sample complexity bounds. These require strategic exploration algorithms that use relatively limited funct…
Zero-Shot Goal-Directed Dialogue via RL on Imagined Conversations
Joey Hong, Sergey Levine, Anca Dragan
Large language models (LLMs) have emerged as powerful and general solutions to many natural language tasks. However, many of the most important applications of language generation…
Offline RL with Observation Histories: Analyzing and Improving Sample Complexity
Joey Hong, Anca Dragan, Sergey Levine
Offline reinforcement learning (RL) can in principle synthesize more optimal behavior from a dataset consisting only of suboptimal trials. One way that this can happen is by "stitc…
Managing extreme AI risks amid rapid progress
Yoshua Bengio, Geoffrey Hinton, Andrew Yao +22
Artificial Intelligence (AI) is progressing rapidly, and companies are shifting their focus to developing generalist AI systems that can autonomously act and pursue goals. Increase…