Samin Yeasar Arnob | DeepAI

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∙ 12/29/2022

Offline Policy Optimization in RL with Variance Regularizaton

Learning policies from fixed offline datasets is a key challenge to scal...

0 Riashat Islam, et al. ∙

∙ 12/28/2022

Representation Learning in Deep RL via Discrete Information Bottleneck

Several self-supervised representation learning methods have been propos...

0 Riashat Islam, et al. ∙

∙ 12/31/2021

Single-Shot Pruning for Offline Reinforcement Learning

Deep Reinforcement Learning (RL) is a powerful framework for solving com...

1 Samin Yeasar Arnob, et al. ∙

∙ 12/31/2021

Importance of Empirical Sample Complexity Analysis for Offline Reinforcement Learning

We hypothesize that empirically studying the sample complexity of offlin...

0 Samin Yeasar Arnob, et al. ∙

∙ 05/03/2020

Off-Policy Adversarial Inverse Reinforcement Learning

Adversarial Imitation Learning (AIL) is a class of algorithms in Reinfor...

0 Samin Yeasar Arnob, et al. ∙

∙ 12/11/2019

Doubly Robust Off-Policy Actor-Critic Algorithms for Reinforcement Learning

We study the problem of off-policy critic evaluation in several variants...

0 Riashat Islam, et al. ∙

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