111 citations · 144 across the 3 of their papers we have counts for
3 papers
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…
Near Optimal Behavior via Approximate State Abstraction
David Abel, D. Ellis Hershkowitz, Michael L. Littman
The combinatorial explosion that plagues planning and reinforcement learning (RL) algorithms can be moderated using state abstraction. Prohibitively large task representations can…
Agent-Agnostic Human-in-the-Loop Reinforcement Learning
David Abel, John Salvatier, Andreas Stuhlmüller +1
Providing Reinforcement Learning agents with expert advice can dramatically improve various aspects of learning. Prior work has developed teaching protocols that enable agents to l…