Fleet Reinforcement Learning using Dependent Gaussian Processes

Onderzoeksoutput: Other contribution

Samenvatting

Physical systems are progressively moving forward from local controllers towards smarter cloud-based architectures. This allows similar inter-connected reinforcement learning agents to share information in order to obtain a more global perspective on the control task at hand. However, the local context and inherent properties of these agents are in practice not identical, making the approach of naively combining gathered information unsuitable. We propose to detect correlations between the observed dynamics of similar agents through dependent Gaussian processes, allowing us to effectively share information between these agents. We validate our approach in a pendulum swing-up and cart-pole setting. Our approach significantly outperforms the naive method of combining all samples into one model, by quickly and accurately estimating dependencies. In future work, we expect to improve our results by measuring correlations between rewards.
Originele taal-2English
Mijlpalentype toekennenWorkshop Submission
OutputmediaPoster
Aantal pagina's6
StatusPublished - 9 dec 2016

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