Kai Arulkumaran

TalkRL: The Reinforcement Learning Podcast

Kai Arulkumaran is a researcher at Araya in Tokyo.

Featured References

AlphaStar: An Evolutionary Computation Perspective
Kai Arulkumaran, Antoine Cully, Julian Togelius

Analysing Deep Reinforcement Learning Agents Trained with Domain Randomisation
Tianhong Dai, Kai Arulkumaran, Tamara Gerbert, Samyakh Tukra, Feryal Behbahani, Anil Anthony Bharath

Training Agents using Upside-Down Reinforcement Learning
Rupesh Kumar Srivastava, Pranav Shyam, Filipe Mutz, Wojciech Jaśkowski, Jürgen Schmidhuber

Additional References

• Araya
• NNAISENSE
• Kai Arulkumaran on Google Scholar
• https://github.com/Kaixhin/rlenvs
• https://github.com/Kaixhin/Atari
• https://github.com/Kaixhin/Rainbow
• Tschiatschek, S., Arulkumaran, K., Stühmer, J. & Hofmann, K. (2018). Variational Inference for Data-Efficient Model Learning in POMDPs. arXiv:1805.09281.
• Arulkumaran, K., Dilokthanakul, N., Shanahan, M. & Bharath, A. A. (2016). Classifying Options for Deep Reinforcement Learning. International Joint Conference on Artificial Intelligence, Deep Reinforcement Learning Workshop.
• Garnelo, M., Arulkumaran, K. & Shanahan, M. (2016). Towards Deep Symbolic Reinforcement Learning. Annual Conference on Neural Information Processing Systems, Deep Reinforcement Learning Workshop.
• Arulkumaran, K., Deisenroth, M. P., Brundage, M. & Bharath, A. A. (2017). Deep reinforcement learning: A brief survey. IEEE Signal Processing Magazine.
• Agostinelli, A., Arulkumaran, K., Sarrico, M., Richemond, P. & Bharath, A. A. (2019). Memory-Efficient Episodic Control Reinforcement Learning with Dynamic Online k-means. Annual Conference on Neural Information Processing Systems, Workshop on Biological and Artificial Reinforcement Learning.
• Sarrico, M., Arulkumaran, K., Agostinelli, A., Richemond, P. & Bharath, A. A. (2019). Sample-Efficient Reinforcement Learning with Maximum Entropy Mellowmax Episodic Control. Annual Conference on Neural Information Processing Systems, Workshop on Biological and Artificial Reinforcement Learning.