What's New in Deep Learning Research: Learning by Playing

Creating agents that can learn like children is one of the ultimate goals of artificial intelligence. Disciplines such as reinforcement learning(RL) are fully devoted to create self-learning models that can use a combination of punishment and reward feedback to master a new task. However, most RL techniques suffer from two main challenges. One very well known is the exploration-explotaition dilemma in which an agent needs to decide how many resources to dedicate to exploring the environment vs. taking specific actions. The other and far less know challenge of RL methods is what I like to call the prior knowledge imbalance dynamic.