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Kavosh Asadi
Kavosh Asadi
Research Scientist, Amazon Web Services
amazon.com의 이메일 확인됨 - 홈페이지
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Dive into deep learning
A Zhang, ZC Lipton, M Li, AJ Smola
arXiv preprint arXiv:2106.11342, 2021
6232021
Hybrid code networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning
JD Williams, K Asadi, G Zweig
arXiv preprint arXiv:1702.03274, 2017
3542017
An Alternative Softmax Operator for Reinforcement Learning
K Asadi, ML Littman
Proceedings of the 34th International Conference on Machine Learning, 243-252, 2017
1682017
Lipschitz Continuity in Model-based Reinforcement Learning
K Asadi, D Misra, ML Littman
Proceedings of the 35th International Conference on Machine Learning, 2018
1122018
Deepmellow: removing the need for a target network in deep Q-learning
S Kim, K Asadi, M Littman, G Konidaris
Proceedings of the Twenty Eighth International Joint Conference on …, 2019
59*2019
State abstraction as compression in apprenticeship learning
D Abel, D Arumugam, K Asadi, Y Jinnai, ML Littman, LLS Wong
Proceedings of the AAAI Conference on Artificial Intelligence 33, 3134-3142, 2019
442019
Combating the Compounding-Error Problem with a Multi-step Model
K Asadi, D Misra, S Kim, ML Littman
arXiv preprint arXiv:1905.13320, 2019
372019
Mean Actor Critic
K Asadi, C Allen, M Roderick, A Mohamed, G Konidaris, M Littman
arXiv preprint arXiv:1709.00503, 2017
29*2017
Sample-efficient Reinforcement Learning for Dialog Control
K Asadi, JD Williams
arXiv preprint arXiv:1612.06000, 2016
222016
Lipschitz lifelong reinforcement learning
E Lecarpentier, D Abel, K Asadi, Y Jinnai, E Rachelson, ML Littman
Proceedings of the AAAI Conference on Artificial Intelligence 35 (9), 8270-8278, 2021
182021
Deep radial-basis value functions for continuous control
K Asadi, N Parikh, RE Parr, GD Konidaris, ML Littman
Proceedings of the AAAI Conference on Artificial Intelligence, 2021
17*2021
Continuous doubly constrained batch reinforcement learning
R Fakoor, J Mueller, K Asadi, P Chaudhari, AJ Smola
arXiv preprint arXiv:2102.09225, 2021
162021
Equivalence between wasserstein and value-aware model-based reinforcement learning
K Asadi, E Cater, D Misra, ML Littman
FAIM Workshop on Prediction and Generative Modeling in Reinforcement Learning 3, 2018
12*2018
Strengths, weaknesses, and combinations of model-based and model-free reinforcement learning
K Asadi
Department of Computing Science University of Alberta, 2015
11*2015
Mitigating Planner Overfitting in Model-Based Reinforcement Learning
D Arumugam, D Abel, K Asadi, N Gopalan, C Grimm, JK Lee, L Lehnert, ...
arXiv preprint arXiv:1812.01129, 2018
102018
Towards a Simple Approach to Multi-step Model-based Reinforcement Learning
K Asadi, E Cater, D Misra, ML Littman
arXiv preprint arXiv:1811.00128, 2018
82018
Learning State Abstractions for Transfer in Continuous Control
K Asadi, D Abel, ML Littman
arXiv preprint arXiv:2002.05518, 2020
52020
Fair E3: Efficient welfare-centric fair reinforcement learning
C Cousins, K Asadi, ML Littman
5th Multidisciplinary Conference on Reinforcement Learning and Decision …, 2022
32022
Faster deep reinforcement learning with slower online network
K Asadi, R Fakoor, O Gottesman, T Kim, M Littman, AJ Smola
Advances in Neural Information Processing Systems 35, 19944-19955, 2022
1*2022
Coarse-Grained Smoothness for RL in Metric Spaces
O Gottesman, K Asadi, C Allen, S Lobel, G Konidaris, M Littman
arXiv preprint arXiv:2110.12276, 2021
12021
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