11 citations · 16 across the 3 of their papers we have counts for
7 papers
Lipschitz Lifelong Reinforcement Learning
Erwan Lecarpentier, David Abel, Kavosh Asadi +3
We consider the problem of knowledge transfer when an agent is facing a series of Reinforcement Learning (RL) tasks. We introduce a novel metric between Markov Decision Processes (…
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…
AlphaX: eXploring Neural Architectures with Deep Neural Networks and Monte Carlo Tree Search
Linnan Wang, Yiyang Zhao, Yuu Jinnai +2
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…
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…
Neural Architecture Search using Deep Neural Networks and Monte Carlo Tree Search
Linnan Wang, Yiyang Zhao, Yuu Jinnai +2
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…
A Survey of Parallel A*
Alex Fukunaga, Adi Botea, Yuu Jinnai +1
A* is a best-first search algorithm for finding optimal-cost paths in graphs. A* benefits significantly from parallelism because in many applications, A* is limited by memory usage…