# Model-based Policy Search for Partially Measurable Systems

arXiv: https://arxiv.org/abs/2101.08740 · HTML: https://mlsanity.com/paper/2101.08740/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 16 references that are in the map.
- First posted on arXiv 21 Jan 2021.

## Builds on (most cited first, 8 of 8)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [PIPPS: Flexible Model-Based Policy Search Robust to the Curse of Chaos](https://mlsanity.com/paper/1902.01240/) (2019)
- [Black-Box Data-efficient Policy Search for Robotics](https://mlsanity.com/paper/1703.07261/) (2017)
- [Derivative-free online learning of inverse dynamics models](https://mlsanity.com/paper/1809.05074/) (2018)
- [Semiparametrical Gaussian Processes Learning of Forward Dynamical Models for Navigating in a Circular Maze](https://mlsanity.com/paper/1809.04993/) (2018)
- [Model-Based Reinforcement Learning for Physical Systems Without Velocity and Acceleration Measurements](https://mlsanity.com/paper/2002.10621/) (2020)
- [A novel Multiplicative Polynomial Kernel for Volterra series identification](https://mlsanity.com/paper/1905.07960/) (2019)

## Abstract (arXiv)

In this paper, we propose a Model-Based Reinforcement Learning (MBRL) algorithm for Partially Measurable Systems (PMS), i.e., systems where the state can not be directly measured, but must be estimated through proper state observers. The proposed algorithm, named Monte Carlo Probabilistic Inference for Learning COntrol for Partially Measurable Systems (MC-PILCO4PMS), relies on Gaussian Processes (GPs) to model the system dynamics, and on a Monte Carlo approach to update the policy parameters. W.r.t. previous GP-based MBRL algorithms, MC-PILCO4PMS models explicitly the presence of state observers during policy optimization, allowing to deal PMS. The effectiveness of the proposed algorithm has been tested both in simulation and in two real systems.