Metaverse Simulation Based on VR, Blockchain, and Reinforcement Learning Model

Abstract

Abstract: With advancements in virtual reality and the boom of the Metaverse, the technology underlying modern games is progressing rapidly. Virtual reality and current Artificial Intelligence algorithms have resulted in impressive results and immersive user experiences. This proposed work aims to create a first-person blockchain figure VR environment incorporating reinforcement learning and imitation learning methods. Supervised and reinforcement learning work using input-output mapping, but the critical difference is that reinforcement learning uses reward and penalty, which are positive and negative action indicators, respectively. The blockchain-based non-fungible agent, based on this indication, learns which actions are favourable. For a better experience, these profitable actions with imitation learning in the state action format are mapped further. In this environment, reinforcement learning is trained with various parameters such as Sensory complexity, Logic complexity for solving tasks, social complexity, and Physical complexity. This simulation enables users to use NFT guns to shoot enemy agents. These agents have been trained to navigate the map using Reinforcement and Imitation learning and shoot us whenever they sense our presence. The simulation is complete if the user kills every agent before his health level finishes. This proposed work highlights the potential of VR, Blockchain, and reinforcement learning in making Metaverse more interactive and impressive