Deep Value-Based Reinforcement Learning

The previous chapter introduced the field of classic reinforcement learning. We learned about agents and environments, and about states, actions, values, and policy functions. We also saw our first planning and learning algorithms: value iteration, SARSA, and Q-learning. The methods in the previous chapter were exact, tabular, methods, which work for problems of moderate size that fit in memory.