360 citations · 475 across the 14 of their papers we have counts for
8 papers
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…
Proving Theorems using Incremental Learning and Hindsight Experience Replay
Eser Aygün, Laurent Orseau, Ankit Anand +5
Traditional automated theorem provers for first-order logic depend on speed-optimized search and many handcrafted heuristics that are designed to work best over a wide range of dom…
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…
A Canonical Form for Weighted Automata and Applications to Approximate Minimization
Borja Balle, Prakash Panangaden, Doina Precup
We study the problem of constructing approximations to a weighted automaton. Weighted finite automata (WFA) are closely related to the theory of rational series. A rational series…
Learning with Pseudo-Ensembles
Philip Bachman, Ouais Alsharif, Doina Precup
We formalize the notion of a pseudo-ensemble, a (possibly infinite) collection of child models spawned from a parent model by perturbing it according to some noise process. E.g., d…