4 citations · 12 across the 8 of their papers we have counts for
8 papers
Exploring Sparse Adapters for Scalable Merging of Parameter Efficient Experts
Samin Yeasar Arnob, Zhan Su, Minseon Kim +6
Merging parameter-efficient task experts has recently gained growing attention as a way to build modular architectures that can be rapidly adapted on the fly for specific downstrea…
Sparse-Reg: Improving Sample Complexity in Offline Reinforcement Learning using Sparsity
Samin Yeasar Arnob, Scott Fujimoto, Doina Precup
In this paper, we investigate the use of small datasets in the context of offline reinforcement learning (RL). While many common offline RL benchmarks employ datasets with over a m…
Offline Policy Optimization in RL with Variance Regularizaton
Riashat Islam, Samarth Sinha, Homanga Bharadhwaj +6
Learning policies from fixed offline datasets is a key challenge to scale up reinforcement learning (RL) algorithms towards practical applications. This is often because off-policy…
Representation Learning in Deep RL via Discrete Information Bottleneck
Riashat Islam, Hongyu Zang, Manan Tomar +8
Several self-supervised representation learning methods have been proposed for reinforcement learning (RL) with rich observations. For real-world applications of RL, recovering und…
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…
Off-Policy Adversarial Inverse Reinforcement Learning
Samin Yeasar Arnob
Adversarial Imitation Learning (AIL) is a class of algorithms in Reinforcement learning (RL), which tries to imitate an expert without taking any reward from the environment and do…
Doubly Robust Off-Policy Actor-Critic Algorithms for Reinforcement Learning
Riashat Islam, Raihan Seraj, Samin Yeasar Arnob +1
We study the problem of off-policy critic evaluation in several variants of value-based off-policy actor-critic algorithms. Off-policy actor-critic algorithms require an off-policy…