Bad-Policy Density: A Measure of Reinforcement Learning Hardness

no code implementations • 7 Oct 2021 • David Abel, Cameron Allen, Dilip Arumugam, D. Ellis Hershkowitz, Michael L. Littman, Lawson L. S. Wong

We address this question by proposing a simple measure of reinforcement-learning hardness called the bad-policy density.

reinforcement-learning Reinforcement Learning (RL)

Finding Options that Minimize Planning Time

no code implementations • 16 Oct 2018 • Yuu Jinnai, David Abel, D. Ellis Hershkowitz, Michael Littman, George Konidaris

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 maximum of value-iteration passes.

Near Optimal Behavior via Approximate State Abstraction

1 code implementation • 15 Jan 2017 • 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.

reinforcement-learning Reinforcement Learning (RL)