Reinforcement Learning in Hyperbolic Spaces: Models and Experiments
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Abstract (arXiv)
We examine five setups where an agent (or two agents) seeks to explore unknown environment without any prior information. Although seemingly very different, all of them can be formalized as Reinforcement Learning (RL) problems in hyperbolic spaces. More precisely, it is natural to endow the action spaces with the hyperbolic metric. We introduce statistical and dynamical models necessary for addressing problems of this kind and implement algorithms based on this framework. Throughout the paper we view RL through the lens of the black-box optimization.
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References · 14
- 2021Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gaugescited by 1,131
- 2017Poincar'e Embeddings for Learning Hierarchical Representationscited by 674
- 2017Evolution Strategies as a Scalable Alternative to Reinforcement Learningcited by 555
- 2018Hyperbolic Neural Networkscited by 393
- 2018Learning Continuous Hierarchies in the Lorentz Model of Hyperbolic Geometrycited by 261
- 2018Representation Tradeoffs for Hyperbolic Embeddingscited by 204
- 2020Machine Learning on Graphs: A Model and Comprehensive Taxonomycited by 156
- 2018Poincar'e GloVe: Hyperbolic Word Embeddingscited by 155
- 2020Lorentz Group Equivariant Neural Network for Particle Physicscited by 120
- 2019Scalable Hyperbolic Recommender Systemscited by 19
- 2020Recent advances in directional statisticscited by 14
- 2022Bingham Policy Parameterization for 3D Rotations in Reinforcement Learningcited by 7
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