Introduction: Reinforcement Learning, Elements of Reinforcement Learning, Limitations and Scope, An Extended Example- Tic-Tac-Toe. 

Multi-armed Bandits: A k-armed Bandit Problem , Action-value Methods, The 10-armed Testbed, Incremental Implementation, Tracking a Nonstationary Problem, Optimistic Initial Values, Upper-Confidence-Bound Action Selection, Gradient Bandit Algorithms.

Finite Markov Decision Processes: The Agent–Environment Interface, Goals and Rewards, Returns and Episodes , Unified Notation for Episodic and Continuing Tasks, Policies and Value Functions, Optimal Policies and Optimal Value Functions, Optimality and Approximation. Review of Markov process and Dynamic Programming.

Temporal-Difference Learning: TD Prediction, Advantages of TD Prediction Methods, Optimality of TD, Sarsa: On-policy TD Control, Q-learning: Policy TD Control. Expected Sarsa. Maximization Bias and Double Learning. 

Eligibility Traces, Functional Approximation, Fitted Q, DQN & Policy Gradient for Full RL and Hierarchical RL. 

Text Book: 

1. Richard S. Sutton and Andrew G. Barto, Reinforcement Learning:An Introduction, second edition, MIT Press, 2019.

References:

1. Phil Winder, Reinforcement Learning, O’Reilly Media Publisher, 2020. 
2. Sudharsan Ravichandiran, Hand-on Reinforcement Learning with Python, Packt Publications, 2018.
3. Sayon Dutta, Reinforcement Learning with Tensor Flow: A beginner’s guide, Packt Publications, 2018.