The REX-D library
This is a C++ library that implements algorithms that combine Reinforcement Learning and Active Learning. It has the following features:
- The V-MIN  and the REX-D  algorithm.
- An algorithm to learn models with action and exogenous effects from a set of input state transitions. The learned models can be used by standard probabilistic task planners .
- Integrates Tobias Lang's implementation of Pasula et al.'s learner  and PRADA  planner.
- Provides a wrapper for IPPC planners. It currently works with the PROST  and the G-PACK  planners, but it should be easy to integrate with other planners.
- Implements teacher guidance  to facilitate interaction with a teacher.
The code is available at bitbucket.
Documentation for the code is available here.
Considerations about the library
- Most features should work in the master branch.
- A few features such as subgoals may only work in the v1.0 or previous tags.
- Teacher guidance only works with the G-PACK planner.
- Exogenous effects only work with the PROST planner.
 V-MIN: Efficient reinforcement learning through demonstrations and relaxed reward demands D. Martínez, G. Alenyà, and C. Torras Proceedings of the AAAI Conference on Artificial Intelligence, 2015, pp. 2857–2863  Relational reinforcement learning with guided demonstrations D. Martínez, G. Alenyà, and C. Torras Artificial Intelligence, 247: 295-312, 2017  Learning Relational Dynamics of Stochastic Domains for Planning D. Martínez, G. Alenyà, C. Torras, T. Ribeiro and K. Inoue International Conference on Automated Planning and Scheduling, 2016, pp. 235-243  Learning symbolic models of stochastic domains H. M. Pasula, L. S. Zettlemoyer and L. P. Kaelbling Journal of Artificial Intelligence Research, 2007, 29(1), pp. 309–352  Planning with noisy probabilistic relational rules T. Lang, M. Toussaint The Journal of Machine Learning Research, 2012, 39, pp. 1–49  PROST: Probabilistic Planning Based on UCT T. Keller and P. Eyerich International Conference on Automated Planning and Scheduling, 2012, pp. 119–127  LRTDP Versus UCT for Online Probabilistic Planning A. Kolobov, Mausam, and D. S. Weld Proceedings of the AAAI Conference on Artificial Intelligence, 2012, pp. 1786–1792