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Abstract
This paper presents a reinforcement learning algorithm that enables fast learning of control policies based on a limited amount of training data, by leveraging the attributes of both model-based and model-free algorithms. This is accomplished by using expert demonstrations for initializing the reinforcement learning algorithm, by learning a Gaussian process model and a policy that behaves similar to the expert. The policy is subsequently improved using Bi-poplation Covariance Matrix Adaptation Evolution Strategy (BIPOP-CMA-ES) that exploits the model in a black-box optimizer. Finally, the policy parameters obtained from BIPOP-CMA-ES are refined by a model-free reinforcement learning algorithm. Scalable Variational Gaussian Processes are used in the model to allow high-dimensional state spaces and larger amounts of data; in addition, autoencoders are used for dimensionality reduction of the parameter space in BIPOP-CMA-ES. The algorithm is tested in a cart-pole system as well in a higher-dimensional industrial peg-in-hole task and is compared to state-of-the-art model-free and model-based algorithms. The proposed algorithm solves the peg-in-hole task faster than previous algorithms.
Originalsprog | Engelsk |
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Titel | 2023 European Control Conference (ECC) |
Antal sider | 6 |
Forlag | IEEE |
Publikationsdato | 2023 |
ISBN (Elektronisk) | 978-3-907144-08-4 |
DOI | |
Status | Udgivet - 2023 |
Begivenhed | 2023 European Control Conference, ECC 2023 - Bucharest, Rumænien Varighed: 13. jun. 2023 → 16. jun. 2023 |
Konference
Konference | 2023 European Control Conference, ECC 2023 |
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Land/Område | Rumænien |
By | Bucharest |
Periode | 13/06/2023 → 16/06/2023 |