5 papers
Successor Feature Sets: Generalizing Successor Representations Across Policies
Kianté Brantley, Soroush Mehri, Geoffrey J. Gordon
Successor-style representations have many advantages for reinforcement learning: for example, they can help an agent generalize from past experience to new goals, and they have bee…
Active Imitation Learning with Noisy Guidance
Kianté Brantley, Amr Sharaf, Hal Daumé
Imitation learning algorithms provide state-of-the-art results on many structured prediction tasks by learning near-optimal search policies. Such algorithms assume training-time ac…
Reinforcement Learning with Convex Constraints
Sobhan Miryoosefi, Kianté Brantley, Hal Daumé +2
In standard reinforcement learning (RL), a learning agent seeks to optimize the overall reward. However, many key aspects of a desired behavior are more naturally expressed as cons…
Non-Monotonic Sequential Text Generation
Sean Welleck, Kianté Brantley, Hal Daumé +1
Standard sequential generation methods assume a pre-specified generation order, such as text generation methods which generate words from left to right. In this work, we propose a…
The UMD Neural Machine Translation Systems at WMT17 Bandit Learning Task
Amr Sharaf, Shi Feng, Khanh Nguyen +2
We describe the University of Maryland machine translation systems submitted to the WMT17 German-English Bandit Learning Task. The task is to adapt a translation system to a new do…