9 citations · 12 across the 4 of their papers we have counts for
4 papers
Coagent Networks: Generalized and Scaled
James E. Kostas, Scott M. Jordan, Yash Chandak +5
Coagent networks for reinforcement learning (RL) [Thomas and Barto, 2011] provide a powerful and flexible framework for deriving principled learning rules for arbitrary stochastic…
Offline Reinforcement Learning for Mixture-of-Expert Dialogue Management
Dhawal Gupta, Yinlam Chow, Aza Tulepbergenov +2
Reinforcement learning (RL) has shown great promise for developing dialogue management (DM) agents that are non-myopic, conduct rich conversations, and maximize overall user satisf…
A Mixture-of-Expert Approach to RL-based Dialogue Management
Yinlam Chow, Aza Tulepbergenov, Ofir Nachum +3
Despite recent advancements in language models (LMs), their application to dialogue management (DM) problems and ability to carry on rich conversations remain a challenge. We use r…
Gradient Temporal-Difference Learning with Regularized Corrections
Sina Ghiassian, Andrew Patterson, Shivam Garg +3
It is still common to use Q-learning and temporal difference (TD) learning-even though they have divergence issues and sound Gradient TD alternatives exist-because divergence seems…