Deep neural networks have achieved outstanding performance in many machine learning tasks. However, this remarkable success is achieved in a closed and static environment where the model is trained using large training data of a single task and deployed for testing on data with a similar distribution. Once the model is deployed, it becomes fixed and inflexible to new knowledge. This contradicts real-world applications, in which agents interact with open and dynamic environments and deal with non-stationary data. This Ph.D. research aims to propose efficient approaches that can develop intelligent agents capable of accumulating new knowledge and adapting to new environments without forgetting the previously learned ones.