First steps toward deep reinforcement learning

This lecture covers the transition from traditional reinforcement learning methods to deep reinforcement learning, focusing on the use of artificial neural networks to learn policies directly without relying on Q-values and V-values. Topics include the application of deep neural networks in games like Chess and Go, backpropagation for deep Q-learning, error functions for continuous input representation, and the use of deep neural networks for value function approximation. The lecture also discusses the challenges of modeling input space for control problems and the comparison between TD learning methods and Policy Gradient approaches.