Interactive Learning

Predictive Probabilistic Merging of Policies (PPMP)

On Github

Reference:

Jan Scholten, Daan Wout, Carlos Celemin, and Jens Kober. Deep Reinforcement Learning with Feedback-based Exploration. arXiv:1903.06151 [cs.LG], 2019. [bibtex] [pdf] [url]

Gaussian Process Coach (GPC)

On Github

Reference:

Daan Wout, Jan Scholten, Carlos Celemin, and Jens Kober. Learning Gaussian Policies from Corrective Human Feedback. arXiv:1903.05216 [cs.LG], 2019. [bibtex] [pdf] [url]

Deep COACH (D-COACH)

On Github

Reference:

Rodrigo Pérez Dattari, Carlos Celemin, Javier Ruiz Del Solar, and Jens Kober. Interactive Learning with Corrective Feedback for Policies based on Deep Neural Networks. In International Symposium on Experimental Robotics (ISER), 2018. [bibtex] [pdf]

Deep Reinforcement Learning

Experience Selection Baselines

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. [bibtex] [pdf] [url]

Policy Search

Policy learning by Weighting Exploration with the Returns (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. [bibtex] [pdf] [doi]

Others

DMPs 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), pp. 69–82, 2010. [bibtex] [pdf] [doi]