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Reinforcement Learning as a tool to make people move to a specific location in Immersive Virtual Reality

机译：强化学习作为一种工具，使人们移动到沉浸式虚拟现实中的特定位置

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摘要

This paper describes the use of Reinforcement Learning in Immersive Virtual Reality to make a person move to a specific location in a virtual environment. Reinforcement Learning is a sub-area of Machine Learning in which an active entity called an agent interacts with its environment and learns how to act in order to achieve a predetermined goal. The Reinforcement Learning had no prior model of behaviour and the participants no prior knowledge that their task was to move to and stay in a specific place. The participants were placed in a virtual environment where they had to avoid collisions with virtual projectiles. Following each projectile the agent analysed the movement made by the participant to determine paths of future projectiles in order to increase the chance of driving participants to the goal position and make them stay there as long as possible. The experiment was carried out with 30 participants, 10 were guided towards the leftmost part of the environment, 10 to the rightmost area, and 10 were used as control group where the projectiles were shot randomly throughout the game. Our results show that people tended to stay close to the target area in both the Left and Right conditions, but not in the Random condition.
机译：本文介绍了在沉浸式虚拟现实中使用加强学习，使人们移动到虚拟环境中的特定位置。加固学习是机器学习的子区域，其中称为代理的活动实体与其环境交互，并学习如何采取行动以实现预定目标。加强学习没有现有行为模型和参与者没有先验知识，即他们的任务是搬到并留在特定的地方。将参与者置于虚拟环境中，他们必须避免与虚拟射弹碰撞。在每个射弹之后，代理人分析了参与者制定的运动，以确定未来射弹的路径，以便增加驾驶参与者到目标位置的机会，并尽可能长的留下它们。该实验与30名参与者进行，10人被引导到环境的最左边部分，10到最右边的区域，10个被用作对照组，其中射弹在整个游戏中随机拍摄。我们的结果表明，人们倾向于在左右条件下保持靠近目标区域，但不在随机条件下。

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    Aitor Rovira; Mel Slater;

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