360 citations · 400 across the 11 of their papers we have counts for
4 papers · 1 filter
Single-Shot Pruning for Offline Reinforcement Learning
Samin Yeasar Arnob, Riyasat Ohib, Sergey Plis +1
Deep Reinforcement Learning (RL) is a powerful framework for solving complex real-world problems. Large neural networks employed in the framework are traditionally associated with…
Importance of Empirical Sample Complexity Analysis for Offline Reinforcement Learning
Samin Yeasar Arnob, Riashat Islam, Doina Precup
We hypothesize that empirically studying the sample complexity of offline reinforcement learning (RL) is crucial for the practical applications of RL in the real world. Several rec…
Flexible Option Learning
Martin Klissarov, Doina Precup
Temporal abstraction in reinforcement learning (RL), offers the promise of improving generalization and knowledge transfer in complex environments, by propagating information more…
Practical Kernel-Based Reinforcement Learning
André M. S. Barreto, Doina Precup, Joelle Pineau
Kernel-based reinforcement learning (KBRL) stands out among reinforcement learning algorithms for its strong theoretical guarantees. By casting the learning problem as a local kern…