Learning from Observation and from Practice Using Behavioral Primitives

We describe a memory-based approach to learning how to select and provide sub-goals for behavioral primitives, given an existing library of primitives. We demonstrate both learning from observation and learning from practice on a marble maze task, Labyrinth.

[1]  Matthew T. Mason,et al.  An exploration of sensorless manipulation , 1986, IEEE J. Robotics Autom..

[2]  Ronald C. Arkin,et al.  An Behavior-based Robotics , 1998 .

[3]  Andrew W. Moore,et al.  Locally Weighted Learning , 1997, Artificial Intelligence Review.

[4]  R. Magill Motor Learning And Control , 1980 .

[5]  Thomas G. Dietterich What is machine learning? , 2020, Archives of Disease in Childhood.

[6]  W. Shebilske,et al.  Motor Learning and Control , 1993 .

[7]  Peter Dayan,et al.  Q-learning , 1992, Machine Learning.

[8]  Sridhar Mahadevan,et al.  Recent Advances in Hierarchical Reinforcement Learning , 2003, Discret. Event Dyn. Syst..

[9]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .