Robots can perfectly follow a set of commands to achieve a certain task. Up till now most of these commands are being programmed by hand. To create a robust control system for robots there is a need for a more effective learning system that can deal with unanticipated events and uncertainties in the robot’s environment. In this project the researchers aim create such control system by combining or integrate reinforcement learning (RL) strategies with deep learning strategies (DL). Reinforcement strategies determine what should be learned and the deep learning strategies model the neural network which can be used for learning. This  works well in a game environments. However, that doesn’t directly transfer to learning real-world control tasks. The research so far has shown that the decision on what to remember and what to learn from those memories  is also a fundamental aspect for more robust control. Do we want robots with a perfect memories, a memory that only remembers the last hour or should it also remember its mistakes to be able to learn from them?