Efficient Exploration for Model-Based Reinforcement Learning

强化学习（RL）是人工智能（AI）研究的一个分支，专注于开发算法，使智能体能够在其所处的环境中学习特定任务，例如最大化游戏中的得分。这个学习过程的一个关键组成部分是让代理探索导致最佳得分的策略。当这些代理不能有效地探索时，它们的适用性对于许多用例来说变得非常有限。拟议的研究将集中在开发新的方法，使RL代理有效地执行探索，使用最有前途的方向的子领域：基于模型的RL。成功完成后，这项研究将对游戏行业产生宝贵的影响，因为它将允许RL系统的可行开发，这些系统可以在许多类型的游戏中获得高水平的游戏技能，使它们能够被用作具有挑战性的对手，并进行广泛的游戏测试。

Reinforcement Learning (RL) is a branch of Artificial Intelligence (AI) research that focuses on developing algorithms which enable an agent to learn a certain task in the environment they are placed in, such as maximizing the score in a game. A crucial component of this learning process is for the agent to explore strategies that result in the best possible score. When these agents are not able to explore efficiently, their applicability becomes very limited for many use cases. The proposed study will focus on developing novel approaches that allow RL agents to efficiently perform exploration, using the most promising direction of the sub-field: model-based RL. On successful completion, this study will be of invaluable impact to the gaming industry, as it will allow feasible development of RL systems that can acquire high-level playing skills in many types of games, allowing them to be used as challenging opponents, and for extensive game testing.