What is the relation between back-propagation and reinforcement learning?
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Backpropagation is a subroutine often used when training Artificial Neural Networks with a Gradient Descent learning algorithm. Gradient Descent requires computation of the error gradient, i.e. derivatives, of a cost function with respect to the network parameters. BP allows you to find this gradient a lot faster than using naive methods. Reinforcement Learning refers to inferring "optimal" behavior, i.e. a strategy, of an agent maximizing some goal in an environment. Depending on the specific RL algorithm, BP may be employed to adjust parameters of a function approximator used to represent aspects of the environment and or the agent.
