So far, we have seen the advancements of reinforcement learning in AlphaGo, autonomous driving, portfolio management, and a lot more. Studies and research say that reinforcement learning can provide features of cognition such as animal behavior.
A close comparison with cognitive science would be the many successful implementations of reinforcement learning in dynamic robotic systems and autonomous driving. They have proved the theory behind applying reinforcement learning algorithms for real-time control of physical systems.
The use of neural networks in deep Q-networks and policy gradients removes the use of hand engineered policy and state representations. The direct implementation of CNNs in deep reinforcement learning and using image pixels as states instead of hand engineered features, became a widely accepted practice. The concept of mini batch training and separate primary and target networks brought success to deep reinforcement learning...