Determinantal Reinforcement Learning with Techniques to Avoid Poor Local Optima

Author:

Osogami Takayuki,Raymond Rudy

Publisher

Springer Singapore

Reference16 articles.

1. Belabbas MA, Wolfe PJ (2009) On landmark selection and sampling in high-dimensional data analysis. Philos Trans R Soc: Math Phys Eng Sci 367:4295–4312

2. Gillenwater J (2014)Approximate inference for determinantal point processes. Ph.D. thesis

3. Hausknecht M, Stone P (2015) Deep recurrent Q-learning for partially observable MDPs. In: Sequential decision making for intelligent agents: papers from the AAAI 2015 fall symposium, pp 29–37

4. Heess N, Silver D, Teh YW (2013) Actor-critic reinforcement learning with energy-based policies. In: Proceedings of the 10th European workshop on reinforcement learning, vol 24., Edinburgh, Scotland, pp 45–58

5. Kang B (2013) Fast determinantal point process sampling with application to clustering. Adv Neural Inf Process Syst 26:2319–2327

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