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Open-Ended Learning Strategies for Learning Complex Locomotion Skills
Joaquin Vanschoren
Event URL: https://openreview.net/forum?id=l8c9NYgA4Lw »

Teaching robots to learn diverse locomotion skills under complex three-dimensional environmental settings via Reinforcement Learning (RL) is still challenging. It has been shown that training agents in simple settings before moving them on to complex settings improves the training process, but so far only in the context of relatively simple locomotion skills. In this work, we adapt the Enhanced Paired Open-Ended Trailblazer (ePOET) approach to train more complex agents to walk efficiently on complex three-dimensional terrains. First, to generate more rugged and diverse three-dimensional training terrains with increasing complexity, we extend the Compositional Pattern Producing Networks - Neuroevolution of Augmenting Topologies (CPPN-NEAT) approach and include randomized shapes. Second, we combine ePOET with Soft Actor-Critic off-policy optimization, yielding ePOET-SAC, to ensure that the agent could learn more diverse skills to solve more challenging tasks.

Author Information

Joaquin Vanschoren (Eindhoven University of Technology)
Joaquin Vanschoren

Joaquin Vanschoren is an Assistant Professor in Machine Learning at the Eindhoven University of Technology. He holds a PhD from the Katholieke Universiteit Leuven, Belgium. His research focuses on meta-learning and understanding and automating machine learning. He founded and leads OpenML.org, a popular open science platform that facilitates the sharing and reuse of reproducible empirical machine learning data. He obtained several demo and application awards and has been invited speaker at ECDA, StatComp, IDA, AutoML@ICML, CiML@NIPS, AutoML@PRICAI, MLOSS@NIPS, and many other occasions, as well as tutorial speaker at NIPS and ECMLPKDD. He was general chair at LION 2016, program chair of Discovery Science 2018, demo chair at ECMLPKDD 2013, and co-organizes the AutoML and meta-learning workshop series at NIPS 2018, ICML 2016-2018, ECMLPKDD 2012-2015, and ECAI 2012-2014. He is also editor and contributor to the book 'Automatic Machine Learning: Methods, Systems, Challenges'.

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