Implementations to supplement my reading of "Reinforcement Learning: An Introduction" by Richard S. Sutton and Andrew Barto.

Ch 2 - Multi-armed Bandits

Ch 4 - Dynamic Programming

Ch 5 - Monte Carlo Methods

Ch 6 - Temporal-Difference Learning

Ch 7 - Multi-step Bootstrapping

Ch 8 - Planning and Learning with Tabular Methods

dqn - My own experiments in using q-networks to achieve generalization in more complex environments

environments - A collection of environment (fMDP) implementations. These environments extend OpenAI Gym's Environment class.