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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 Associate Professor in Machine Learning at the Eindhoven University of Technology. He holds a PhD from the Katholieke Universiteit Leuven, Belgium. His research focuses on understanding and automating machine learning, meta-learning, and continual learning. He founded and leads OpenML.org, a popular open science platform with over 250,000 users that facilitates the sharing and reuse of machine learning datasets and models. He is a founding member of the European AI networks ELLIS and CLAIRE, and an active member of MLCommons. He obtained several awards, including an Amazon Research Award, an ECMLPKDD Best Demo award, and the Dutch Data Prize. He was a tutorial speaker at NeurIPS 2018 and AAAI 2021, and gave over 30 invited talks. He co-initiated the NeurIPS Datasets and Benchmarks track and was NeurIPS Datasets and Benchmarks Chair from 2021 to 2023. He also co-organized the AutoML workshop series at ICML, and the Meta-Learning workshop series at NeurIPS. He is editor-in-chief of DMLR (part of JMLR), as well as an action editor for JMLR and machine learning moderator for ArXiv. He authored and co-authored over 150 scientific papers, as well as reference books on Automated Machine Learning and Meta-learning.

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