Experience selection in deep reinforcement learning for control

On Github

Reference: Tim de Bruin, Jens Kober, Karl Tuyls, and Robert Babuška. Experience selection in deep reinforcement learning for control. Journal of Machine Learning Research, 19(9):1--56, 2018. [ bib | http ]

Motor primitives for hitting and batting

A basic MATLAB/Octave implementation: hittingMP.m

Reference: Jens Kober, Katharina Muelling, Oliver Kroemer, Christoph H. Lampert, Bernhard Schölkopf, and Jan Peters. Movement templates for learning of hitting and batting. In IEEE International Conference on Robotics and Automation (ICRA), pages 69--82, 2010. [ bib | .pdf ]

PoWER

A basic MATLAB/Octave implementation of Policy learning by Weighting Exploration with the Returns (PoWER) [4 variants: return or state-action value function, constant exploration or automatic adaptation], episodic Reward-Weighted Regression (eRWR), episodic Natural Actor Critic (eNAC), ‘Vanilla’ Policy Gradients (VPG), and Finite Difference Gradients (FDG): matlab_PoWER.zip

The required motor primitive code can be downloaded from http://www-clmc.usc.edu/Resources/Software

Reference: Jens Kober and Jan Peters. Policy search for motor primitives in robotics. Machine Learning, 84(1-2):171--203, 2011. [ bib | .pdf ]