PILCO

PILCO: A Model-Based and Data-Efficient Approach to Policy Search. PILCO policy search framework (Matlab version). This software package implements the PILCO RL policy search framework. The learning framework can be applied to MDPs with continuous states and controls/actions and is based on probabilistic modeling of the dynamics and approximate Bayesian inference for policy evaluation and improvement.


References in zbMATH (referenced in 21 articles )

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  1. Hayashi, Akinobu; A. Ruiken, Dirk; Hasegawa, Tadaaki; Goerick, Christian: Reasoning about uncertain parameters and agent behaviors through encoded experiences and belief planning (2020)
  2. Lazaridis, Aristotelis; Fachantidis, Anestis; Vlahavas, Ioannis: Deep reinforcement learning: a state-of-the-art walkthrough (2020)
  3. Millidge, Beren: Deep active inference as variational policy gradients (2020)
  4. Moriconi, Riccardo; Kumar, K. S. Sesh; Deisenroth, Marc Peter: High-dimensional Bayesian optimization with projections using quantile Gaussian processes (2020)
  5. Paul, Supratik; Chatzilygeroudis, Konstantinos; Ciosek, Kamil; Mouret, Jean-Baptiste; Osborne, Michael A.; Whiteson, Shimon: Robust reinforcement learning with Bayesian optimisation and quadrature (2020)
  6. Boutselis, George I.; Pan, Yunpeng; Theodorou, Evangelos A.: Numerical trajectory optimization for stochastic mechanical systems (2019)
  7. Zhao, Dongfang; Liu, Jiafeng; Wu, Rui; Cheng, Dansong; Tang, Xianglong: An active exploration method for data efficient reinforcement learning (2019)
  8. Akrour, Riad; Abdolmaleki, Abbas; Abdulsamad, Hany; Peters, Jan; Neumann, Gerhard: Model-free trajectory-based policy optimization with monotonic improvement (2018)
  9. Joseph, Ajin George; Bhatnagar, Shalabh: An incremental off-policy search in a model-free Markov decision process using a single sample path (2018)
  10. Moerland, Thomas M.; Broekens, Joost; Jonker, Catholijn M.: Emotion in reinforcement learning agents and robots: a survey (2018)
  11. Murray, Ryan; Palladino, Michele: A model for system uncertainty in reinforcement learning (2018)
  12. Agostini, Alejandro; Celaya, Enric: Online reinforcement learning using a probability density estimation (2017)
  13. Kupcsik, Andras; Deisenroth, Marc Peter; Peters, Jan; Poh, Loh Ai; Vadakkepat, Prahlad; Neumann, Gerhard: Model-based contextual policy search for data-efficient generalization of robot skills (2017)
  14. van Hoof, Herke; Neumann, Gerhard; Peters, Jan: Non-parametric policy search with limited information loss (2017)
  15. Wirth, Christian; Akrour, Riad; Neumann, Gerhard; F├╝rnkranz, Johannes: A survey of preference-based reinforcement learning methods (2017)
  16. Kamalapurkar, Rushikesh; Rosenfeld, Joel A.; Dixon, Warren E.: Efficient model-based reinforcement learning for approximate online optimal control (2016)
  17. Kamalapurkar, Rushikesh; Walters, Patrick; Dixon, Warren E.: Model-based reinforcement learning for approximate optimal regulation (2016)
  18. Yang, Xiaoke; Maciejowski, Jan M.: Fault tolerant control using Gaussian processes and model predictive control (2015)
  19. Jung, Tobias; Wehenkel, Louis; Ernst, Damien; Maes, Francis: Optimized look-ahead tree policies: a bridge between look-ahead tree policies and direct policy search (2014)
  20. Tangkaratt, Voot; Mori, Syogo; Zhao, Tingting; Morimoto, Jun; Sugiyama, Masashi: Model-based policy gradients with parameter-based exploration by least-squares conditional density estimation (2014)

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