360 citations · 2.7k across the 150 of their papers we have counts for
25 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…
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…
Constructing a Good Behavior Basis for Transfer using Generalized Policy Updates
Safa Alver, Doina Precup
We study the problem of learning a good set of policies, so that when combined together, they can solve a wide variety of unseen reinforcement learning tasks with no or very little…
On the Expressivity of Markov Reward
David Abel, Will Dabney, Anna Harutyunyan +4
Reward is the driving force for reinforcement-learning agents. This paper is dedicated to understanding the expressivity of reward as a way to capture tasks that we would want an a…