这是我在Doina Precupp教授领导下的项目。摘要：自主机器人在特定任务中实现了高水平的性能和可靠性。然而，机器人代理能够适应新环境并学习不同任务非常重要。在强化学习中，学习者通过与环境交互和收集数据来学习。因此，就计算机和机器人的实际物理成本而言，物理代理对孤立地学习不同任务可能是非常昂贵的。在这种情况下，可以使用转移学习来在模拟环境中学习并在实际物理机器人上使用学习的知识来避免损坏以及加速vanilla RL算法。本报告重点介绍了强化学习领域中的转移学习问题，并将其应用于机器人导航任务。
This was the project I did under Prof. Doina Precupp.
Abstract: Autonomous robots have achieved high levels of performance and reliability at specific tasks. However it is important for a robot agent to be able to adapt to the new environment and learn varying tasks. In Reinforcement learning agent learns by interacting with the environment and gathering data. Therefore learning different tasks in isolation can be very expensive for a physical agent both in terms of computation and actual physical cost of the Robot. Transfer learning can be used in such cases to learn in simulated environment and using the learned knowledge on actual physical robot to avoid damage as well as to speed up vanilla RL algorithms. This report focuses on understanding transfer learning problem in reinforcement learning domain and applying it to robot navigation task.