Abstract
In this article usage of procedural generation for reinforcement learning is discussed. Procedural generation is a technique in computer graphics and game design that creates content algorithmically, often generating terrain, levels, or assets dynamically, rather than relying on pre-made, static data. Thus, in the context of reinforcement learning for robots, procedural generation can be used to create dynamic and diverse training environments, allowing robots to learn and adapt to a wide range of scenarios through trial and error. Making it possible to train robots in a virtual sophisticated environment resembling what is present in the world. Keywords: Reinforcement Learning (RL), Procedural Generation (PG), Unity3D, Environment, Robotics.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
