32 citations · 61 across the 7 of their papers we have counts for
5 papers · 1 filter
The Efficiency of Human Cognition Reflects Planned Information Processing
Mark K. Ho, David Abel, Jonathan D. Cohen +2
Planning is useful. It lets people take actions that have desirable long-term consequences. But, planning is hard. It requires thinking about consequences, which consumes limited c…
Discovering Options for Exploration by Minimizing Cover Time
Yuu Jinnai, Jee Won Park, David Abel +1
One of the main challenges in reinforcement learning is solving tasks with sparse reward. We show that the difficulty of discovering a distant rewarding state in an MDP is bounded…
Finding Options that Minimize Planning Time
Yuu Jinnai, David Abel, D Ellis Hershkowitz +2
We formalize the problem of selecting the optimal set of options for planning as that of computing the smallest set of options so that planning converges in less than a given maxim…
Modeling Latent Attention Within Neural Networks
Christopher Grimm, Dilip Arumugam, Siddharth Karamcheti +3
Deep neural networks are able to solve tasks across a variety of domains and modalities of data. Despite many empirical successes, we lack the ability to clearly understand and int…
Exploratory Gradient Boosting for Reinforcement Learning in Complex Domains
David Abel, Alekh Agarwal, Fernando Diaz +2
High-dimensional observations and complex real-world dynamics present major challenges in reinforcement learning for both function approximation and exploration. We address both of…