Reinforcement learning: Model-free MC learner with code implementation

Today we focus on building a Monte Carlo (MC) agent to learn a MDP. In a previous story, we implemented a model-based ADP learner which estimates a model of reward function r(s) and transition probabilities p(s′ s, a). This model-based approach may work efficiently in some cases. However, if the transition model is difficult to estimate, a model-free approach tends to be a better choice. Monte Carlo (MC), which is our topic today, is one example of such model-free approaches. The code in this story is part of our MAD from scratch project where MAD stands for machine learning, artificial intelligence, and data science. In model-based methods, our policy is derived from the utility values of states.