Reinforcement Learning Part 3 – Challenges & Considerations

Summary: In the first part of this series we described the basics of Reinforcement Learning (RL). In this article we describe how deep learning is augmenting RL and a variety of challenges and considerations that need to be addressed in each implementation. In the first part of this series, Understanding Basic RL Models we described the basics of how reinforcement learning (RL) models are constructed and interpreted. RL systems can be constructed using policy gradient techniques which attempt to learn by directly mapping an observation to an action (the automated house look up table). Or they can be constructed using Q-Learning in which we train a neural net to calculate the estimated Q factor on the fly which is used when the state space gets large and complex.