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Harm van Seijen
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2020 – today
- 2024
- [c22]Harry Zhao, Safa Alver, Harm van Seijen, Romain Laroche, Doina Precup, Yoshua Bengio:
Consciousness-Inspired Spatio-Temporal Abstractions for Better Generalization in Reinforcement Learning. ICLR 2024 - 2023
- [c21]Nathaniel Weir, Xingdi Yuan, Marc-Alexandre Côté, Matthew J. Hausknecht, Romain Laroche, Ida Momennejad, Harm van Seijen, Benjamin Van Durme:
One-Shot Learning from a Demonstration with Hierarchical Latent Language. AAMAS 2023: 2388-2390 - [c20]Ali Rahimi-Kalahroudi, Janarthanan Rajendran, Ida Momennejad, Harm van Seijen, Sarath Chandar:
Replay Buffer with Local Forgetting for Adapting to Local Environment Changes in Deep Model-Based Reinforcement Learning. CoLLAs 2023: 21-42 - [c19]Riashat Islam, Manan Tomar, Alex Lamb, Yonathan Efroni, Hongyu Zang, Aniket Rajiv Didolkar, Dipendra Misra, Xin Li, Harm van Seijen, Remi Tachet des Combes, John Langford:
Principled Offline RL in the Presence of Rich Exogenous Information. ICML 2023: 14390-14421 - [i18]Ali Rahimi-Kalahroudi, Janarthanan Rajendran, Ida Momennejad, Harm van Seijen, Sarath Chandar:
Replay Buffer With Local Forgetting for Adaptive Deep Model-Based Reinforcement Learning. CoRR abs/2303.08690 (2023) - [i17]Mingde Zhao, Safa Alver, Harm van Seijen, Romain Laroche, Doina Precup, Yoshua Bengio:
Combining Spatial and Temporal Abstraction in Planning for Better Generalization. CoRR abs/2310.00229 (2023) - 2022
- [c18]Shangtong Zhang, Romain Laroche, Harm van Seijen, Shimon Whiteson, Remi Tachet des Combes:
A Deeper Look at Discounting Mismatch in Actor-Critic Algorithms. AAMAS 2022: 1491-1499 - [c17]Jorge A. Mendez, Harm van Seijen, Eric Eaton:
Modular Lifelong Reinforcement Learning via Neural Composition. ICLR 2022 - [c16]Yi Wan, Ali Rahimi-Kalahroudi, Janarthanan Rajendran, Ida Momennejad, Sarath Chandar, Harm van Seijen:
Towards Evaluating Adaptivity of Model-Based Reinforcement Learning Methods. ICML 2022: 22536-22561 - [i16]Nathaniel Weir, Xingdi Yuan, Marc-Alexandre Côté, Matthew J. Hausknecht, Romain Laroche, Ida Momennejad, Harm van Seijen, Benjamin Van Durme:
One-Shot Learning from a Demonstration with Hierarchical Latent Language. CoRR abs/2203.04806 (2022) - [i15]Yi Wan, Ali Rahimi-Kalahroudi, Janarthanan Rajendran, Ida Momennejad, Sarath Chandar, Harm van Seijen:
Towards Evaluating Adaptivity of Model-Based Reinforcement Learning Methods. CoRR abs/2204.11464 (2022) - [i14]Jorge A. Mendez, Harm van Seijen, Eric Eaton:
Modular Lifelong Reinforcement Learning via Neural Composition. CoRR abs/2207.00429 (2022) - [i13]Riashat Islam
, Manan Tomar, Alex Lamb, Yonathan Efroni, Hongyu Zang, Aniket Didolkar, Dipendra Misra, Xin Li, Harm van Seijen, Remi Tachet des Combes, John Langford:
Agent-Controller Representations: Principled Offline RL with Rich Exogenous Information. CoRR abs/2211.00164 (2022) - 2021
- [c15]Faruk Ahmed, Yoshua Bengio, Harm van Seijen, Aaron C. Courville:
Systematic generalisation with group invariant predictions. ICLR 2021 - [c14]Sungryull Sohn, Sungtae Lee, Jongwook Choi, Harm van Seijen, Mehdi Fatemi, Honglak Lee:
Shortest-Path Constrained Reinforcement Learning for Sparse Reward Tasks. ICML 2021: 9780-9790 - [i12]Sungryull Sohn, Sungtae Lee, Jongwook Choi, Harm van Seijen, Mehdi Fatemi, Honglak Lee:
Shortest-Path Constrained Reinforcement Learning for Sparse Reward Tasks. CoRR abs/2107.06405 (2021) - 2020
- [c13]Harm van Seijen, Hadi Nekoei, Evan Racah, Sarath Chandar:
The LoCA Regret: A Consistent Metric to Evaluate Model-Based Behavior in Reinforcement Learning. NeurIPS 2020 - [i11]Harm van Seijen, Hadi Nekoei, Evan Racah, Sarath Chandar:
The LoCA Regret: A Consistent Metric to Evaluate Model-Based Behavior in Reinforcement Learning. CoRR abs/2007.03158 (2020) - [i10]Shangtong Zhang, Romain Laroche, Harm van Seijen, Shimon Whiteson, Remi Tachet des Combes:
A Deeper Look at Discounting Mismatch in Actor-Critic Algorithms. CoRR abs/2010.01069 (2020)
2010 – 2019
- 2019
- [c12]Mehdi Fatemi, Shikhar Sharma, Harm van Seijen, Samira Ebrahimi Kahou:
Dead-ends and Secure Exploration in Reinforcement Learning. ICML 2019: 1873-1881 - [c11]Harm van Seijen, Mehdi Fatemi, Arash Tavakoli:
Using a Logarithmic Mapping to Enable Lower Discount Factors in Reinforcement Learning. NeurIPS 2019: 14111-14121 - [i9]Harm van Seijen, Mehdi Fatemi, Arash Tavakoli:
Using a Logarithmic Mapping to Enable Lower Discount Factors in Reinforcement Learning. CoRR abs/1906.00572 (2019) - 2018
- [c10]Lucas Lehnert, Romain Laroche, Harm van Seijen:
On Value Function Representation of Long Horizon Problems. AAAI 2018: 3457-3465 - [c9]Remi Tachet des Combes, Philip Bachman, Harm van Seijen:
Learning Invariances for Policy Generalization. ICLR (Workshop) 2018 - [c8]Romain Laroche, Harm van Seijen:
In reinforcement learning, all objective functions are not equal. ICLR (Workshop) 2018 - [i8]Remi Tachet des Combes, Philip Bachman, Harm van Seijen:
Learning Invariances for Policy Generalization. CoRR abs/1809.02591 (2018) - 2017
- [c7]Vivek Veeriah, Harm van Seijen, Richard S. Sutton:
Forward Actor-Critic for Nonlinear Function Approximation in Reinforcement Learning. AAMAS 2017: 556-564 - [c6]Harm van Seijen, Mehdi Fatemi, Romain Laroche, Joshua Romoff, Tavian Barnes, Jeffrey Tsang:
Hybrid Reward Architecture for Reinforcement Learning. NIPS 2017: 5392-5402 - [i7]Romain Laroche, Mehdi Fatemi, Joshua Romoff, Harm van Seijen:
Multi-Advisor Reinforcement Learning. CoRR abs/1704.00756 (2017) - [i6]Harm van Seijen, Mehdi Fatemi, Joshua Romoff, Romain Laroche, Tavian Barnes, Jeffrey Tsang:
Hybrid Reward Architecture for Reinforcement Learning. CoRR abs/1706.04208 (2017) - 2016
- [j3]Harm van Seijen, Ashique Rupam Mahmood, Patrick M. Pilarski, Marlos C. Machado
True Online Temporal-Difference Learning. J. Mach. Learn. Res. 17: 145:1-145:40 (2016) - [i5]Harm van Seijen:
Effective Multi-step Temporal-Difference Learning for Non-Linear Function Approximation. CoRR abs/1608.05151 (2016) - [i4]Harm van Seijen, Mehdi Fatemi, Joshua Romoff, Romain Laroche:
Improving Scalability of Reinforcement Learning by Separation of Concerns. CoRR abs/1612.05159 (2016) - 2015
- [i3]Harm van Seijen, Ashique Rupam Mahmood, Patrick M. Pilarski, Richard S. Sutton:
An Empirical Evaluation of True Online TD(λ). CoRR abs/1507.00353 (2015) - [i2]Harm van Seijen, Ashique Rupam Mahmood, Patrick M. Pilarski, Marlos C. Machado, Richard S. Sutton:
True Online Temporal-Difference Learning. CoRR abs/1512.04087 (2015) - 2014
- [j2]Harm van Seijen, Shimon Whiteson, Leon J. H. M. Kester:
Efficient Abstraction Selection in Reinforcement Learning. Comput. Intell. 30(4): 657-699 (2014) - [c5]Harm van Seijen, Richard S. Sutton:
True Online TD(lambda). ICML 2014: 692-700 - 2013
- [c4]Harm van Seijen, Richard S. Sutton:
Planning by Prioritized Sweeping with Small Backups. ICML (3) 2013: 361-369 - [c3]Harm van Seijen, Shimon Whiteson, Leon J. H. M. Kester:
Efficient Abstraction Selection in Reinforcement Learning (Extended Abstract). SARA 2013 - [i1]Harm van Seijen, Richard S. Sutton:
Planning by Prioritized Sweeping with Small Backups. CoRR abs/1301.2343 (2013) - 2011
- [j1]Harm van Seijen, Shimon Whiteson, Hado van Hasselt, Marco A. Wiering:
Exploiting Best-Match Equations for Efficient Reinforcement Learning. J. Mach. Learn. Res. 12: 2045-2094 (2011) - 2010
- [p1]Harm van Seijen, Shimon Whiteson, Leon J. H. M. Kester:
Switching between Representations in Reinforcement Learning. Interactive Collaborative Information Systems 2010: 65-84
2000 – 2009
- 2009
- [c2]Harm van Seijen, Hado van Hasselt, Shimon Whiteson, Marco A. Wiering:
A theoretical and empirical analysis of Expected Sarsa. ADPRL 2009: 177-184 - [c1]Harm van Seijen, Shimon Whiteson:
Postponed Updates for Temporal-Difference Reinforcement Learning. ISDA 2009: 665-672
Coauthor Index
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last updated on 2024-10-07 21:13 CEST by the dblp team
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