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Parameterized MDPs and Reinforcement Learning Problems -- A Maximum
  Entropy Principle Based Framework
v1v2v3 (latest)

Parameterized MDPs and Reinforcement Learning Problems -- A Maximum Entropy Principle Based Framework

17 June 2020
Amber Srivastava
S. Salapaka
ArXiv (abs)PDFHTML

Papers citing "Parameterized MDPs and Reinforcement Learning Problems -- A Maximum Entropy Principle Based Framework"

5 / 5 papers shown
Autonomy-Aware Clustering: When Local Decisions Supersede Global Prescriptions
Autonomy-Aware Clustering: When Local Decisions Supersede Global Prescriptions
Amber Srivastava
S. Basiri
S. Salapaka
OffRL
230
0
0
30 Sep 2025
Parametrized Multi-Agent Routing via Deep Attention Models
Parametrized Multi-Agent Routing via Deep Attention Models
S. Basiri
Dhananjay Tiwari
S. Salapaka
175
1
0
30 Jul 2025
Bridging the Gap between Newton-Raphson Method and Regularized Policy
  Iteration
Bridging the Gap between Newton-Raphson Method and Regularized Policy Iteration
Zeyang Li
Chuxiong Hu
Yunan Wang
Tianze Zhu
Jie Li
Shengbo Eben Li
188
0
0
11 Oct 2023
On the Importance of Exploration for Real Life Learned Algorithms
On the Importance of Exploration for Real Life Learned AlgorithmsInternational Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2022
Steffen Gracla
C. Bockelmann
Armin Dekorsy
198
1
0
21 Apr 2023
Reinforcement Learning With Sparse-Executing Actions via Sparsity
  Regularization
Reinforcement Learning With Sparse-Executing Actions via Sparsity RegularizationIEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2021
Jing-Cheng Pang
Tian Xu
Shengyi Jiang
Yu-Ren Liu
Yang Yu
347
2
0
18 May 2021
1
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