Reinforcement Learning in POMDPs With Memoryless Options and Option-Observation Initiation Sets

Authors

  • Denis Steckelmacher Vrije Universiteit Brussels
  • Diederik Roijers Vrije Universiteit Brussels
  • Anna Harutyunyan Vrije Universiteit Brussels
  • Peter Vrancx PROWLER.io
  • Hélène Plisnier Vrije Universiteit Brussels
  • Ann Nowé Vrije Universiteit Brussels

Keywords:

Reinforcement Learning, Options, Hierarchical Reinforcement Learning, Partially Observable MDP

Abstract

Many real-world reinforcement learning problems have a hierarchical nature, and often exhibit some degree of partial observability. While hierarchy and partial observability are usually tackled separately (for instance by combining recurrent neural networks and options), we show that addressing both problems simultaneously is simpler and more efficient in many cases. More specifically, we make the initiation set of options conditional on the previously-executed option, and show that options with such Option-Observation Initiation Sets (OOIs) are at least as expressive as Finite State Controllers (FSCs), a state-of-the-art approach for learning in POMDPs. OOIs are easy to design based on an intuitive description of the task, lead to explainable policies and keep the top-level and option policies memoryless. Our experiments show that OOIs allow agents to learn optimal policies in challenging POMDPs, while being much more sample-efficient than a recurrent neural network over options.

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Published

2018-04-29

How to Cite

Steckelmacher, D., Roijers, D., Harutyunyan, A., Vrancx, P., Plisnier, H., & Nowé, A. (2018). Reinforcement Learning in POMDPs With Memoryless Options and Option-Observation Initiation Sets. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/11606