Applications d'un environnement d'apprentissage par renforcement avec un humain dans la boucle

学徒环境中对人类和圆球强化的应用

• 批准号: 549202-2019
• 负责人: Haughey, Roger
• 金额: $ 1.82万
• 依托单位: Cégep John Abbott College
• 依托单位国家: 加拿大
• 项目类别: Applied Research and Development Grants - Level 1
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=700249
• 关键词: Applications; un; environnement; apprentissage; par

Reinforcement learning is learning what to do - how to map situations to actions - so as to maximize a numerical reward signal. The learner is not told which actions to take, but instead must discover which actions yield the most reward by trying them. Since RL requires a lot of data, therefore it is most applicable where simulated data is readily available like gameplay, robotics. Here, we consider specifically systems that interact with users in the loop. When a human is involved, there are recurring issues: there is rarely a good simulator, the systems are stochastic and non-stationary, and running them is expensive and/or slow. This is very different from training on a simulated environment where data is effectively unlimited, consequences for poor actions are non-existent and system dynamics are clean and often deterministic. An open-source framework for such a training environment is proposed by the company AI-R, and aims at solving problems of training reinforcement learning algorithms with a human in the loop: non-rewindability, delayed reward functions and unbalanced relevance. The goal of this research project is to use their framework to train a learning agent to teach a human., and we will try to demonstrate that we can effectively teach a human player to play the game Smash Bros. This would enable academics in various fields to use this framework for research where a human behaviour is studied, and open the door to a range of new products and business models for entrepreneurs to develop.

强化学习是学习该做什么——如何将情况映射到行动——从而最大化数字奖励信号。学习者不会被告知采取哪些行动，而是必须通过尝试来发现哪些行动会产生最大的回报。因为强化学习需要大量的数据，所以它最适用于随时可以获得模拟数据的地方，比如游戏玩法、机器人技术。