A theoretical and empirical analysis of Expected Sarsa. - Harm van Seijen; Hado van Hasselt; Shimon Whiteson; Marco A. Wiering (ADPRL, 2009)

Postponed Updates for Temporal-Difference Reinforcement Learning. - Harm van Seijen; Shimon Whiteson (ISDA, 2009)

Switching between Representations in Reinforcement Learning. - Harm van Seijen; Shimon Whiteson; Leon J. H. M. Kester (Interactive Collaborative Information Systems, 2010)

Exploiting Best-Match Equations for Efficient Reinforcement Learning. - Harm van Seijen; Shimon Whiteson; Hado van Hasselt; Marco A. Wiering (J. Mach. Learn. Res., 2011)

Planning by Prioritized Sweeping with Small Backups. - Harm van Seijen; Richard S. Sutton (ICML (3), 2013)

Efficient Abstraction Selection in Reinforcement Learning (Extended Abstract). - Harm van Seijen; Shimon Whiteson; Leon J. H. M. Kester (SARA, 2013)

True Online TD(lambda). - Harm van Seijen; Richard S. Sutton (ICML, 2014)

Efficient Abstraction Selection in Reinforcement Learning. - Harm van Seijen; Shimon Whiteson; Leon J. H. M. Kester (Comput. Intell., 2014)

An Empirical Evaluation of True Online TD(λ). - Harm van Seijen; Ashique Rupam Mahmood; Patrick M. Pilarski; Richard S. Sutton (CoRR, 2015)

Effective Multi-step Temporal-Difference Learning for Non-Linear Function Approximation. - Harm van Seijen (CoRR, 2016)

Improving Scalability of Reinforcement Learning by Separation of Concerns. - Harm van Seijen; Mehdi Fatemi; Joshua Romoff; Romain Laroche (CoRR, 2016)

True Online Temporal-Difference Learning. - Harm van Seijen; Ashique Rupam Mahmood; Patrick M. Pilarski; Marlos C. Machado; Richard S. Sutton (J. Mach. Learn. Res., 2016)

Forward Actor-Critic for Nonlinear Function Approximation in Reinforcement Learning. - Vivek Veeriah; Harm van Seijen; Richard S. Sutton (AAMAS, 2017)

Hybrid Reward Architecture for Reinforcement Learning. - Harm van Seijen; Mehdi Fatemi; Romain Laroche; Joshua Romoff; Tavian Barnes; Jeffrey Tsang (NIPS, 2017)

Multi-Advisor Reinforcement Learning. - Romain Laroche; Mehdi Fatemi; Joshua Romoff; Harm van Seijen (CoRR, 2017)

On Value Function Representation of Long Horizon Problems. - Lucas Lehnert; Romain Laroche; Harm van Seijen (AAAI, 2018)

Learning Invariances for Policy Generalization. - Remi Tachet des Combes; Philip Bachman; Harm van Seijen (ICLR (Workshop), 2018)

In reinforcement learning, all objective functions are not equal. - Romain Laroche; Harm van Seijen (ICLR (Workshop), 2018)

Dead-ends and Secure Exploration in Reinforcement Learning. - Mehdi Fatemi; Shikhar Sharma 001; Harm van Seijen; Samira Ebrahimi Kahou (ICML, 2019)

Using a Logarithmic Mapping to Enable Lower Discount Factors in Reinforcement Learning. - Harm van Seijen; Mehdi Fatemi; Arash Tavakoli (NeurIPS, 2019)

The LoCA Regret: A Consistent Metric to Evaluate Model-Based Behavior in Reinforcement Learning. - Harm van Seijen; Hadi Nekoei; Evan Racah; Sarath Chandar (NeurIPS, 2020)

Systematic generalisation with group invariant predictions. - Faruk Ahmed; Yoshua Bengio; Harm van Seijen; Aaron C. Courville (ICLR, 2021)

Shortest-Path Constrained Reinforcement Learning for Sparse Reward Tasks. - Sungryull Sohn; Sungtae Lee; Jongwook Choi; Harm van Seijen; Mehdi Fatemi; Honglak Lee (ICML, 2021)

A Deeper Look at Discounting Mismatch in Actor-Critic Algorithms. - Shangtong Zhang; Romain Laroche; Harm van Seijen; Shimon Whiteson; Remi Tachet des Combes (AAMAS, 2022)

Modular Lifelong Reinforcement Learning via Neural Composition. - Jorge A. Mendez; Harm van Seijen; Eric Eaton (ICLR, 2022)

Towards Evaluating Adaptivity of Model-Based Reinforcement Learning Methods. - Yi Wan; Ali Rahimi-Kalahroudi; Janarthanan Rajendran; Ida Momennejad; Sarath Chandar; Harm van Seijen (ICML, 2022)

Agent-Controller Representations: Principled Offline RL with Rich Exogenous Information. - Riashat Islam; Manan Tomar; Alex Lamb; Yonathan Efroni; Hongyu Zang; Aniket Didolkar; Dipendra Misra; Xin Li 033; Harm van Seijen; Remi Tachet des Combes; John Langford 001 (CoRR, 2022)

One-Shot Learning from a Demonstration with Hierarchical Latent Language. - Nathaniel Weir; Xingdi Yuan; Marc-Alexandre Côté; Matthew J. Hausknecht; Romain Laroche; Ida Momennejad; Harm van Seijen; Benjamin Van Durme (AAMAS, 2023)

Replay Buffer with Local Forgetting for Adapting to Local Environment Changes in Deep Model-Based Reinforcement Learning. - Ali Rahimi-Kalahroudi; Janarthanan Rajendran; Ida Momennejad; Harm van Seijen; Sarath Chandar (CoLLAs, 2023)

Principled Offline RL in the Presence of Rich Exogenous Information. - Riashat Islam; Manan Tomar; Alex Lamb; Yonathan Efroni; Hongyu Zang; Aniket Rajiv Didolkar; Dipendra Misra; Xin Li 033; Harm van Seijen; Remi Tachet des Combes; John Langford 001 (ICML, 2023)

Replay Buffer With Local Forgetting for Adaptive Deep Model-Based Reinforcement Learning. - Ali Rahimi-Kalahroudi; Janarthanan Rajendran; Ida Momennejad; Harm van Seijen; Sarath Chandar (CoRR, 2023)

Combining Spatial and Temporal Abstraction in Planning for Better Generalization. - Mingde Zhao 001; Safa Alver; Harm van Seijen; Romain Laroche; Doina Precup; Yoshua Bengio (CoRR, 2023)

Consciousness-Inspired Spatio-Temporal Abstractions for Better Generalization in Reinforcement Learning. - Harry Zhao 001; Safa Alver; Harm van Seijen; Romain Laroche; Doina Precup; Yoshua Bengio (ICLR, 2024)