Transfer learning for continual learning in non-stationary environments

现有的人工智能算法在其应用环境发生根本性变化或被赋予新的任务时需要重新训练。因此，建立能够在不断变化的情况下可靠学习的自动算法的能力将在该领域树立新的标准。该项目的主要目标是开发新的框架，在该框架中，强化学习算法可以在随机环境中的一组类似任务中更稳定地执行并实现更高的样本和计算效率。也就是说，我们打算使用迁移学习来解决非平稳环境中的持续学习问题。为此，我们提出以下三个目标：

Currently available artificial intelligence algorithms require re-training whenever their application environment undergoes fundamental changes and a new task is given. The ability to build an automated algorithm that can learn reliably under changing situations will therefore set new standards in the field. The main goal of this project is to develop new frameworks in which reinforcement learning algorithms can perform more stably and achieve higher sample and computational efficiency among a set of similar tasks in stochastic environment. That is, we intend to use transfer learning to address continual learning in non-stationary environments. To do so, we propose the following three objectives: 1) We will build adaptive neural network-based reinforcement learning models that will construct a knowledge representation that fits the non-stationary environmental complexity. This can be understood as a particular type of continual learning. 2) We will evaluate the performance of the developed frameworks/models in real-world applications which will be identified in collaboration with LG Electronics Canada. The identified problems may involve vision inspection data as inputs. 3) We will investigate how these frameworks/models perform when transferred to new environments in terms of dynamics, rewards and goals. The transfer learning frameworks/models will be combined with the developed continual learning frameworks/models. To sum up, the proposed project is to advance the current state-of-the-art of reinforcement learning so as to improve the sample and computational efficiency across similarly configured tasks and to robustify its performance in non-stationary environment.