ROBOLEAP Journal Papers
- Pajarinen, J.; Thai, H.L.; Akrour, R.; Peters, J.; Neumann, G. (2019). Compatible natural gradient policy search, Machine Learning (MLJ), 108, 8, pp.1443--1466, Springer.
ROBOLEAP Conference and Workshop Papers
- Lauri, M.; Pajarinen, J.; Peters, J. (2019). Information gathering in decentralized POMDPs by policy graph improvement, Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS).
- Akrour, R.; Pajarinen, J.; Neumann, G.; Peters, J. (2019). Projections for Approximate Policy Iteration Algorithms, Proceedings of the International Conference on Machine Learning (ICML).
- Hoelscher, J.; Koert, D.; Peters, J.; Pajarinen, J. (2018). Utilizing Human Feedback in POMDP Execution and Specification, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS).
ROBOLEAP Books, Book Chapters & Theses
- Schotschneider, A. (2020). Learning High-Level Behavior for Autonomous Vehicles, Master Thesis.
- Hartmann, V. (2019). Efficient Exploration using Value Bounds in Deep Reinforcement Learning, Master Thesis.
- Zhi, R. (2018). Deep reinforcement learning under uncertainty for autonomous driving, Master Thesis.