Sutton and Barto book implementation

Reinforcement Learning Installation $ python setup.py install Overview This repository contains code that implements algorithms and models from Sutton's book on reinforcement learning. The book, titled "Reinforcement Learning: An Introduction," is a classic text on the subject and provides a comprehensive introduction to the field. The code in this repository is organized into several modules, each of which covers differents topics. Methods Multi Armed Bandits Epsilon Greedy Optimistic Initial Values Gradient α (non stationary) Multi Armed Bandits Model Based Policy Evaluation Policy Iteration Value Iteration Model Based Monte Carlo estimation and control First-visit α-MC Every-visit α-MC MC with Exploring Starts Off-policy MC, ordinary and weighted importance sampling Monte Carlo estimation and control Temporal Difference TD(n) estimation n-step SARSA n-step Q-learning n-step Expected SARSA double Q learning n-step Tree Backup Temporal Difference Planning Dyna-Q/Dyna-Q+ Pri ... Read full article.