Exploration in Reinforcement Learning with Deep Covering Options

no code implementations • ICLR 2020 • Yuu Jinnai, Jee Won Park, Marlos C. Machado, George Konidaris

While many option discovery methods have been proposed to accelerate exploration in reinforcement learning, they are often heuristic.

reinforcement-learning Reinforcement Learning (RL)

Lipschitz Lifelong Reinforcement Learning

1 code implementation • 15 Jan 2020 • Erwan Lecarpentier, David Abel, Kavosh Asadi, Yuu Jinnai, Emmanuel Rachelson, Michael L. Littman

We consider the problem of knowledge transfer when an agent is facing a series of Reinforcement Learning (RL) tasks.

reinforcement-learning Reinforcement Learning (RL) +1

AlphaX: eXploring Neural Architectures with Deep Neural Networks and Monte Carlo Tree Search

1 code implementation • 26 Mar 2019 • Linnan Wang, Yiyang Zhao, Yuu Jinnai, Yuandong Tian, Rodrigo Fonseca

Neural Architecture Search (NAS) has shown great success in automating the design of neural networks, but the prohibitive amount of computations behind current NAS methods requires further investigations in improving the sample efficiency and the network evaluation cost to get better results in a shorter time.

Image Captioning Neural Architecture Search +4

Discovering Options for Exploration by Minimizing Cover Time

no code implementations • 2 Mar 2019 • Yuu Jinnai, Jee Won Park, David Abel, George Konidaris

One of the main challenges in reinforcement learning is solving tasks with sparse reward.

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.

Policy and Value Transfer in Lifelong Reinforcement Learning

no code implementations • ICML 2018 • David Abel, Yuu Jinnai, Sophie Yue Guo, George Konidaris, Michael Littman

We consider the problem of how best to use prior experience to bootstrap lifelong learning, where an agent faces a series of task instances drawn from some task distribution.

reinforcement-learning Reinforcement Learning (RL)

Neural Architecture Search using Deep Neural Networks and Monte Carlo Tree Search

2 code implementations • 18 May 2018 • Linnan Wang, Yiyang Zhao, Yuu Jinnai, Yuandong Tian, Rodrigo Fonseca

Neural Architecture Search (NAS) has shown great success in automating the design of neural networks, but the prohibitive amount of computations behind current NAS methods requires further investigations in improving the sample efficiency and the network evaluation cost to get better results in a shorter time.

Image Captioning Neural Architecture Search +4

A Survey of Parallel A*

1 code implementation • 16 Aug 2017 • Alex Fukunaga, Adi Botea, Yuu Jinnai, Akihiro Kishimoto

A* is a best-first search algorithm for finding optimal-cost paths in graphs.

On Hash-Based Work Distribution Methods for Parallel Best-First Search

no code implementations • 10 Jun 2017 • Yuu Jinnai, Alex Fukunaga

We show that Abstract Zobrist hashing outperforms previous methods on search domains using hand-coded, domain specific feature projection functions.

graph partitioning