ROBOLEAP Journal Papers

Pajarinen, J.; Thai, H.L.; Akrour, R.; Peters, J.; Neumann, G. (2019). Compatible natural gradient policy search, Machine Learning, Springer.   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]

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).   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]

Akrour, R.; Pajarinen, J.; Neumann, G.; Peters, J. (2019). Projections for Approximate Policy Iteration Algorithms, Proceedings of the International Conference on Machine Learning (ICML).   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]

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).   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]

ROBOLEAP Books, Book Chapters & Theses

Hartmann, V. (2019). Efficient Exploration using Value Bounds in Deep Reinforcement Learning, Master Thesis.   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]

Zhi, R. (2018). Deep reinforcement learning under uncertainty for autonomous driving, Master Thesis.   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]

  

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