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Alekh Agarwal
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Publications
- 2024
- [j13]Aditya Modi, Jinglin Chen, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal:
Model-Free Representation Learning and Exploration in Low-Rank MDPs. J. Mach. Learn. Res. 25: 6:1-6:76 (2024) - 2022
- [c73]Ching-An Cheng, Tengyang Xie, Nan Jiang, Alekh Agarwal:
Adversarially Trained Actor Critic for Offline Reinforcement Learning. ICML 2022: 3852-3878 - [c70]Jinglin Chen, Aditya Modi, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal:
On the Statistical Efficiency of Reward-Free Exploration in Non-Linear RL. NeurIPS 2022 - [i68]Ching-An Cheng, Tengyang Xie, Nan Jiang, Alekh Agarwal:
Adversarially Trained Actor Critic for Offline Reinforcement Learning. CoRR abs/2202.02446 (2022) - [i63]Jinglin Chen, Aditya Modi, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal:
On the Statistical Efficiency of Reward-Free Exploration in Non-Linear RL. CoRR abs/2206.10770 (2022) - 2021
- [c66]Tengyang Xie, Ching-An Cheng, Nan Jiang, Paul Mineiro, Alekh Agarwal:
Bellman-consistent Pessimism for Offline Reinforcement Learning. NeurIPS 2021: 6683-6694 - [i61]Aditya Modi, Jinglin Chen, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal:
Model-free Representation Learning and Exploration in Low-rank MDPs. CoRR abs/2102.07035 (2021) - [i57]Tengyang Xie, Ching-An Cheng, Nan Jiang, Paul Mineiro, Alekh Agarwal:
Bellman-consistent Pessimism for Offline Reinforcement Learning. CoRR abs/2106.06926 (2021) - 2019
- [c55]Wen Sun, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford:
Model-based RL in Contextual Decision Processes: PAC bounds and Exponential Improvements over Model-free Approaches. COLT 2019: 2898-2933 - [c52]Simon S. Du, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal, Miroslav Dudík, John Langford:
Provably efficient RL with Rich Observations via Latent State Decoding. ICML 2019: 1665-1674 - [i45]Simon S. Du, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal, Miroslav Dudík, John Langford:
Provably efficient RL with Rich Observations via Latent State Decoding. CoRR abs/1901.09018 (2019) - 2018
- [c47]Nan Jiang, Alekh Agarwal:
Open Problem: The Dependence of Sample Complexity Lower Bounds on Planning Horizon. COLT 2018: 3395-3398 - [c44]Hoang Minh Le, Nan Jiang, Alekh Agarwal, Miroslav Dudík, Yisong Yue, Hal Daumé III:
Hierarchical Imitation and Reinforcement Learning. ICML 2018: 2923-2932 - [c43]Christoph Dann, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire:
On Oracle-Efficient PAC RL with Rich Observations. NeurIPS 2018: 1429-1439 - [i36]Hoang Minh Le, Nan Jiang, Alekh Agarwal, Miroslav Dudík, Yisong Yue, Hal Daumé III:
Hierarchical Imitation and Reinforcement Learning. CoRR abs/1803.00590 (2018) - [i35]Christoph Dann, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire:
On Polynomial Time PAC Reinforcement Learning with Rich Observations. CoRR abs/1803.00606 (2018) - [i32]Wen Sun, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford:
Model-Based Reinforcement Learning in Contextual Decision Processes. CoRR abs/1811.08540 (2018) - 2017
- [c40]Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire:
Contextual Decision Processes with low Bellman rank are PAC-Learnable. ICML 2017: 1704-1713 - 2016
- [i24]Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire:
Contextual Decision Processes with Low Bellman Rank are PAC-Learnable. CoRR abs/1610.09512 (2016)
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last updated on 2024-09-18 01:11 CEST by the dblp team
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