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 29 articles )

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  1. Itoh, Takeshi D.; Ishihara, Koji; Morimoto, Jun: Implicit contact dynamics modeling with explicit inertia matrix representation for real-time, model-based control in physical environment (2022)
  2. Thomas Pinder; Daniel Dodd: GPJax: A Gaussian Process Framework in JAX (2022) not zbMATH
  3. Abraham, Ian; Broad, Alexander; Pinosky, Allison; Argall, Brenna; Murphey, Todd D.: Hybrid control for learning motor skills (2021)
  4. Hanna, Josiah P.; Desai, Siddharth; Karnan, Haresh; Warnell, Garrett; Stone, Peter: Grounded action transformation for sim-to-real reinforcement learning (2021)
  5. Jafarzadeh, Hassan; Fleming, Cody: DMPC: a data-and model-driven approach to predictive control (2021)
  6. Kroemer, Oliver; Niekum, Scott; Konidaris, George: A review of robot learning for manipulation: challenges, representations, and algorithms (2021)
  7. Villacampa-Calvo, Carlos; Zaldívar, Bryan; Garrido-Merchán, Eduardo C.; Hernández-Lobato, Daniel: Multi-class Gaussian process classification with noisy inputs (2021)
  8. Wilson, James T.; Borovitskiy, Viacheslav; Terenin, Alexander; Mostowsky, Peter; Deisenroth, Marc Peter: Pathwise conditioning of Gaussian processes (2021)
  9. Hayashi, Akinobu; A. Ruiken, Dirk; Hasegawa, Tadaaki; Goerick, Christian: Reasoning about uncertain parameters and agent behaviors through encoded experiences and belief planning (2020)
  10. Lazaridis, Aristotelis; Fachantidis, Anestis; Vlahavas, Ioannis: Deep reinforcement learning: a state-of-the-art walkthrough (2020)
  11. Millidge, Beren: Deep active inference as variational policy gradients (2020)
  12. Moriconi, Riccardo; Kumar, K. S. Sesh; Deisenroth, Marc Peter: High-dimensional Bayesian optimization with projections using quantile Gaussian processes (2020)
  13. Paul, Supratik; Chatzilygeroudis, Konstantinos; Ciosek, Kamil; Mouret, Jean-Baptiste; Osborne, Michael A.; Whiteson, Shimon: Robust reinforcement learning with Bayesian optimisation and quadrature (2020)
  14. Boutselis, George I.; Pan, Yunpeng; Theodorou, Evangelos A.: Numerical trajectory optimization for stochastic mechanical systems (2019)
  15. Zhao, Dongfang; Liu, Jiafeng; Wu, Rui; Cheng, Dansong; Tang, Xianglong: An active exploration method for data efficient reinforcement learning (2019)
  16. Akrour, Riad; Abdolmaleki, Abbas; Abdulsamad, Hany; Peters, Jan; Neumann, Gerhard: Model-free trajectory-based policy optimization with monotonic improvement (2018)
  17. Boris Ivanovic, James Harrison, Apoorva Sharma, Mo Chen, Marco Pavone: BaRC: Backward Reachability Curriculum for Robotic Reinforcement Learning (2018) arXiv
  18. Joseph, Ajin George; Bhatnagar, Shalabh: An incremental off-policy search in a model-free Markov decision process using a single sample path (2018)
  19. Murray, Ryan; Palladino, Michele: A model for system uncertainty in reinforcement learning (2018)
  20. Agostini, Alejandro; Celaya, Enric: Online reinforcement learning using a probability density estimation (2017)

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