Reinforcement Learning Algorithms with Python

History of RL

The first mathematical foundation of RL was built during the 1960s and 1970s in the field of optimal control. This solved the problem of minimizing a behavior's measure of a dynamic system over time. The method involved solving a set of equations with the known dynamics of the system. During this time, the key concept of a Markov decision process (MDP) was introduced. This provides a general framework for modeling decision-making in stochastic situations. During these years, a solution method for optimal control called dynamic programming (DP) was introduced. DP is a method that breaks down a complex problem into a collection of simpler subproblems for solving an MDP.

Note that DP only provides an easier way to solve optimal ...