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.
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