Moving in a Simulated Environment Through Deep Reinforcement Learning

2024-07-232024-07-232022-09-08J. Esarte, P. D. Folino and J. C. Gómez, "Moving in a Simulated Environment Through Deep Reinforcement Learning," 2022 IEEE Biennial Congress of Argentina (ARGENCON), San Juan, Argentina, 2022, pp. 1-6, doi: 10.1109/ARGENCON55245.2022.9939868.978-1-6654-8014-7978-1-6654-8015-4http://hdl.handle.net/20.500.12272/11143Reinforcement learning is a field of artificial intelligence that is continuously evolving and has a wide variety of applications. In recent years major progress has been made in the application of deep reinforcement learning to highdimensional problems with continuous state and action spaces. This paper presents a complete analysis of the application of the soft actor-critic algorithm to teach a four legged robot with three joints on each leg how to move towards the center of a virtually simulated environment. The general formulation of the reinforcement learning problem is first presented, followed by the description of the environment under analysis and the applied algorithm. Afterwards, the obtained results are compared against those of a manually programmed policy, closing with a discussion of some key design choices and common challenges.pdfengopenAccessAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/Attribution-NonCommercial-NoDerivatives 4.0 Internacionaldeep reinforcement learningsoft actor-critictetrapod robotvirtual environmentpredictive controlmachine learningroboticsartificial neural networksMoving in a Simulated Environment Through Deep Reinforcement Learninginfo:eu-repo/semantics/conferenceObjectAtribución (“Creative Commons Attribution”): En cualquier explotación de la obra autorizada por la licencia será necesario reconocer la autoría (obligatoria en todos los casos). No comercial (“Creative Commons Non Commercial”): La explotación de la obra queda limitada a usos no comerciales. Sin obras derivadas (“Creative Commons No Derivate Works”): La autorización para explotar la obra no incluye la posibilidad de crear una obra derivada (traducciones, adaptaciones, etc.).10.1109/ARGENCON55245.2022.9939868