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Kavosh Asadi
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Books and Theses
- 2021
- [b1]Kavosh Asadi:
Smoothness in Reinforcement Learning with Large State and Action Spaces. Brown University, USA, 2021
Conference and Workshop Papers
- 2024
- [c15]Zuxin Liu, Jesse Zhang, Kavosh Asadi, Yao Liu, Ding Zhao, Shoham Sabach, Rasool Fakoor:
TAIL: Task-specific Adapters for Imitation Learning with Large Pretrained Models. ICLR 2024 - 2023
- [c14]Omer Gottesman, Kavosh Asadi, Cameron S. Allen, Samuel Lobel, George Konidaris, Michael Littman:
Coarse-Grained Smoothness for Reinforcement Learning in Metric Spaces. AISTATS 2023: 1390-1410 - [c13]Kavosh Asadi, Rasool Fakoor, Shoham Sabach:
Resetting the Optimizer in Deep RL: An Empirical Study. NeurIPS 2023 - [c12]Kavosh Asadi, Shoham Sabach, Yao Liu, Omer Gottesman, Rasool Fakoor:
TD Convergence: An Optimization Perspective. NeurIPS 2023 - 2022
- [c11]Kavosh Asadi, Rasool Fakoor, Omer Gottesman, Taesup Kim, Michael L. Littman, Alexander J. Smola:
Faster Deep Reinforcement Learning with Slower Online Network. NeurIPS 2022 - [c10]Martin Klissarov, Rasool Fakoor, Jonas W. Mueller, Kavosh Asadi, Taesup Kim, Alexander J. Smola:
Adaptive Interest for Emphatic Reinforcement Learning. NeurIPS 2022 - 2021
- [c9]Kavosh Asadi, Neev Parikh, Ronald E. Parr, George Dimitri Konidaris, Michael L. Littman:
Deep Radial-Basis Value Functions for Continuous Control. AAAI 2021: 6696-6704 - [c8]Erwan Lecarpentier, David Abel, Kavosh Asadi, Yuu Jinnai, Emmanuel Rachelson, Michael L. Littman:
Lipschitz Lifelong Reinforcement Learning. AAAI 2021: 8270-8278 - [c7]Rasool Fakoor, Jonas Mueller, Kavosh Asadi, Pratik Chaudhari, Alexander J. Smola:
Continuous Doubly Constrained Batch Reinforcement Learning. NeurIPS 2021: 11260-11273 - 2019
- [c6]David Abel, Dilip Arumugam, Kavosh Asadi, Yuu Jinnai, Michael L. Littman, Lawson L. S. Wong:
State Abstraction as Compression in Apprenticeship Learning. AAAI 2019: 3134-3142 - [c5]Seungchan Kim, Kavosh Asadi, Michael L. Littman, George Dimitri Konidaris:
Removing the Target Network from Deep Q-Networks with the Mellowmax Operator. AAMAS 2019: 2060-2062 - [c4]Seungchan Kim
, Kavosh Asadi, Michael L. Littman, George Dimitri Konidaris:
DeepMellow: Removing the Need for a Target Network in Deep Q-Learning. IJCAI 2019: 2733-2739 - 2018
- [c3]Kavosh Asadi, Dipendra Misra, Michael L. Littman:
Lipschitz Continuity in Model-based Reinforcement Learning. ICML 2018: 264-273 - 2017
- [c2]Jason D. Williams
, Kavosh Asadi, Geoffrey Zweig:
Hybrid Code Networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning. ACL (1) 2017: 665-677 - [c1]Kavosh Asadi, Michael L. Littman:
An Alternative Softmax Operator for Reinforcement Learning. ICML 2017: 243-252
Informal and Other Publications
- 2024
- [i20]Kavosh Asadi, Yao Liu, Shoham Sabach, Ming Yin, Rasool Fakoor:
Learning the Target Network in Function Space. CoRR abs/2406.01838 (2024) - 2023
- [i19]Kavosh Asadi, Shoham Sabach, Yao Liu, Omer Gottesman, Rasool Fakoor:
TD Convergence: An Optimization Perspective. CoRR abs/2306.17750 (2023) - [i18]Kavosh Asadi, Rasool Fakoor, Shoham Sabach:
Resetting the Optimizer in Deep RL: An Empirical Study. CoRR abs/2306.17833 (2023) - [i17]Zuxin Liu, Jesse Zhang, Kavosh Asadi, Yao Liu, Ding Zhao, Shoham Sabach, Rasool Fakoor:
TAIL: Task-specific Adapters for Imitation Learning with Large Pretrained Models. CoRR abs/2310.05905 (2023) - 2022
- [i16]Zhiyuan Zhou, Cameron Allen, Kavosh Asadi, George Konidaris:
Characterizing the Action-Generalization Gap in Deep Q-Learning. CoRR abs/2205.05588 (2022) - 2021
- [i15]Ishaan Shah, David Halpern, Kavosh Asadi, Michael L. Littman:
Convergence of a Human-in-the-Loop Policy-Gradient Algorithm With Eligibility Trace Under Reward, Policy, and Advantage Feedback. CoRR abs/2109.07054 (2021) - [i14]Omer Gottesman, Kavosh Asadi, Cameron Allen, Sam Lobel, George Konidaris, Michael Littman:
Coarse-Grained Smoothness for RL in Metric Spaces. CoRR abs/2110.12276 (2021) - [i13]Kavosh Asadi, Rasool Fakoor, Omer Gottesman, Michael L. Littman, Alexander J. Smola:
Deep Q-Network with Proximal Iteration. CoRR abs/2112.05848 (2021) - 2020
- [i12]Erwan Lecarpentier, David Abel, Kavosh Asadi, Yuu Jinnai, Emmanuel Rachelson, Michael L. Littman:
Lipschitz Lifelong Reinforcement Learning. CoRR abs/2001.05411 (2020) - [i11]Kavosh Asadi, Ronald E. Parr, George Dimitri Konidaris, Michael L. Littman:
Deep RBF Value Functions for Continuous Control. CoRR abs/2002.01883 (2020) - [i10]Kavosh Asadi, David Abel, Michael Littman:
Learning State Abstractions for Transfer in Continuous Control. CoRR abs/2002.05518 (2020) - 2019
- [i9]Kavosh Asadi, Dipendra Misra, Seungchan Kim, Michael L. Littman:
Combating the Compounding-Error Problem with a Multi-step Model. CoRR abs/1905.13320 (2019) - 2018
- [i8]Kavosh Asadi, Dipendra Misra, Michael L. Littman:
Lipschitz Continuity in Model-based Reinforcement Learning. CoRR abs/1804.07193 (2018) - [i7]Kavosh Asadi, Evan Cater, Dipendra Misra, Michael L. Littman:
Equivalence Between Wasserstein and Value-Aware Model-based Reinforcement Learning. CoRR abs/1806.01265 (2018) - [i6]Kavosh Asadi, Evan Cater, Dipendra Misra, Michael L. Littman:
Towards a Simple Approach to Multi-step Model-based Reinforcement Learning. CoRR abs/1811.00128 (2018) - [i5]Dilip Arumugam, David Abel, Kavosh Asadi, Nakul Gopalan, Christopher Grimm, Jun Ki Lee, Lucas Lehnert, Michael L. Littman:
Mitigating Planner Overfitting in Model-Based Reinforcement Learning. CoRR abs/1812.01129 (2018) - 2017
- [i4]Jason D. Williams, Kavosh Asadi, Geoffrey Zweig:
Hybrid Code Networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning. CoRR abs/1702.03274 (2017) - [i3]Kavosh Asadi, Cameron Allen, Melrose Roderick, Abdel-rahman Mohamed, George Dimitri Konidaris, Michael L. Littman:
Mean Actor Critic. CoRR abs/1709.00503 (2017) - 2016
- [i2]Kavosh Asadi, Michael L. Littman:
A New Softmax Operator for Reinforcement Learning. CoRR abs/1612.05628 (2016) - [i1]Kavosh Asadi, Jason D. Williams:
Sample-efficient Deep Reinforcement Learning for Dialog Control. CoRR abs/1612.06000 (2016)
Coauthor Index
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