Selected Key References

For a more complete list, please see Google Scholar.

Pascal Klink, Carlo D'Eramo, Jan Peters, Joni Pajarinen (2024). On the benefit of optimal transport for curriculum reinforcement learning. IEEE Transactions on Pattern Analysis and Machine Intelligence.

Wenshuai Zhao, Yi Zhao, Zhiyuan Li, Juho Kannala, Joni Pajarinen (2024). Optimistic Multi-Agent Policy Gradient. In Proceedings of the International Conference on Machine Learning (ICML).

Zhiyuan Li, Wenshuai Zhao, Lijun Wu, Joni Pajarinen, Joni Pajarinen (2024). Backpropagation Through Agents. AAAI Conference on Artificial Intelligence (AAAI).

Yi Zhao, Le Chen, Jan Schneider, Quankai Gao, Juho Kannala, Bernhard Schölkopf, Joni Pajarinen, Dieter Büchler (2024). RP1M: A Large-Scale Motion Dataset for Piano Playing with Bi-Manual Dexterous Robot Hands. Conference on Robot Learning (CoRL).

Aidan Scannell, Kalle Kujanpää, Yi Zhao, Mohammadreza Nakhaei, Arno Solin, Joni Pajarinen (2024). iQRL – Implicitly Quantized Representations for Sample-efficient Reinforcement Learning. arXiv preprint arXiv:2406.02696.

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Aidan Scannell, Riccardo Mereu, Paul Chang, Ella Tamir, Joni Pajarinen, Arno Solin (2024). Function-space Prameterization of Neural Networks for Sequential Learning. The Twelth International Conference on Learning Representations (ICLR).

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Kalle Kujanpää, Amin Babadi, Yi Zhao, Juho Kannala, Alexander Ilin, Joni Pajarinen (2024). Continuous Monte Carlo Graph Search. International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS).

Changling Li, Zhang-Wei Hong, Pulkit Agrawal, Divyansh Garg, Joni Pajarinen (2024). ROER: Regularized Optimal Experience Replay. Reinforcement Learning Conference (RLC).

Vivienne Huiling Wang, Tinghuai Wang, Wenyan Yang, Joni-Kristian Kämäräinen, Joni Pajarinen (2024). Probabilistic Subgoal Representations for Hierarchical Reinforcement learning. In Proceedings of the International Conference on Machine Learning (ICML).

Mohammadreza Nakhaei, Aidan Scannell, Joni Pajarinen (2024). Residual Learning and Context Encoding for Adaptive Offline-to-Online Reinforcement Learning. 4th Annual Conference on Learning for Dynamics and Control (L4DC).

Aidan Scannell, Kalle Kujanpää, Yi Zhao, Mohammadreza Nakhaei, Arno Solin, Joni Pajarinen (2024). Quantized Representations Prevent Dimensional Collapse in Self-predictive RL. ICML 2024 Workshop on Aligning Reinforcement Learning Experimentalists and Theorists (ARLET).

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Kalle Kujanpää, Joni Pajarinen, Alexander Ilin (2023). Hybrid search for efficient planning with completeness guarantees. Advances in Neural Information Processing Systems (NeurIPS).

Yi Zhao, Wenshuai Zhao, Rinu Boney, Juho Kannala, Joni Pajarinen (2023). Simplified Temporal Consistency Reinforcement Learning. In Proceedings of the International Conference on Machine Learning (ICML).

Kalle Kujanpää, Joni Pajarinen, Alexander Ilin (2023). Hierarchical Imitation Learning with Vector Quantized Models. In Proceedings of the International Conference on Machine Learning (ICML).

Abdolreza Taheri, Pelle Gustafsson, Marcus Rösth, Reza Ghabcheloo, Joni Pajarinen (2022). Nonlinear Model Learning for Compensation and Feedforward Control of Real-World Hydraulic Actuators Using Gaussian Processes. IEEE Robotics and Automation Letters.

Joni Pajarinen, Jens Lundell, Ville Kyrki (2022). POMDP planning under object composition uncertainty: Application to robotic manipulation. IEEE Transactions on Robotics.

Pascal Klink, Haoyi Yang, Carlo D'Eramo, Joni Pajarinen, Jan Peters (2022). Curriculum reinforcement learning via constrained optimal transport. In Proceedings of the International Conference on Machine Learning (ICML).

Mikko Lauri, David Hsu, Joni Pajarinen (2022). Partially Observable Markov Decision Processes in Robotics: A Survey. IEEE Transactions on Robotics.

Pascal Klink, Carlo D'Eramo, Jan Peters, Joni Pajarinen (2022). Boosted Curriculum Reinforcement Learning. International Conference on Learning Representations (ICLR).

Abdolreza Taheri, Joni Pajarinen, Reza Ghabcheloo (2022). GPU-accelerated policy optimization via batch automatic differentiation of gaussian processes for real-world control. International Conference on Robotics and Automation (ICRA).

Simone Parisi, Davide Tateo, Maximilian Hensel, Carlo D'Eramo, Jan Peters, Joni Pajarinen (2022). Long-Term Visitation Value for Deep Exploration in Sparse-Reward Reinforcement Learning. Algorithms.

Pascal Klink, Hany Abdulsamad, Boris Belousov, Carlo D'Eramo, Jan Peters, Joni Pajarinen (2021). A Probabilistic Interpretation of Self-Paced Learning with Applications to Reinforcement Learning. Journal of Machine Learning Research (JMLR).

Tuan Q Dam, Carlo D'Eramo, Jan Peters, Joni Pajarinen (2021). Convex Regularization in Monte-Carlo Tree Search. In Proceedings of the International Conference on Machine Learning (ICML).

Pascal Klink, Carlo D'Eramo, Jan Peters, Joni Pajarinen (2020). Self-Paced Deep Reinforcement Learning. In Proceedings of the Annual Conference on Neural Information Processing Systems (NeurIPS).

Tuan Dam, Pascal Klink, Carlo D'Eramo, Jan Peters, Joni Pajarinen (2020). Generalized Mean Estimation in Monte-Carlo Tree Search. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI).

Mikko Lauri, Joni Pajarinen, Jan Peters (2020). Multi-agent active information gathering in discrete and continuous-state decentralized POMDPs by policy graph improvement. Autonomous Agents and Multi-Agent Systems.

Joni Pajarinen, Oleg Arenz, Jan Peters, Gerhard Neumann (2020). Probabilistic approach to physical object disentangling. IEEE Robotics and Automation Letters.

Joni Pajarinen, Hong Linh Thai, Riad Akrour, Jan Peters, Gerhard Neumann (2019). Compatible natural gradient policy search. Machine Learning.

Riad Akrour, Joni Pajarinen, Jan Peters, Gerhard Neumann (2019). Projections for Approximate Policy Iteration Algorithms. In Proceedings of the International Conference on Machine Learning (ICML).

Dorothea Koert, Joni Pajarinen, Albert Schotschneider, Susanne Trick, Constantin Rothkopf, Jan Peters (2019). Learning Intention Aware Online Adaptation of Movement Primitives. IEEE Robotics and Automation Letters.

Takayuki Osa, Joni Pajarinen, Gerhard Neumann, J Andrew Bagnell, Pieter Abbeel, Jan Peters (2018). An Algorithmic Perspective on Imitation Learning. Foundations and Trends in Robotics.

Joni Pajarinen, Ville Kyrki, Michael Koval, Siddhartha Srinivasa, Jan Peters, Gerhard Neumann (2017). Hybrid Control Trajectory Optimization under Uncertainty. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

Joni Pajarinen, Ville Kyrki (2017). Robotic manipulation of multiple objects as a POMDP. Artificial Intelligence, Elsevier.

Joni Pajarinen, Jaakko Peltonen (2011). Periodic Finite State Controllers for Efficient POMDP and DEC-POMDP Planning. In Annual Conference on Neural Information Processing Systems (NeurIPS).