Reinforcement Learning
Course Name:
Reinforcement Learning(CS426)
Programme:
B.Tech (CSE)
Semester:
Seventh
Category:
Programme Specific Electives (PSE)
Credits (L-T-P):
04(3-1-0)
Content:
Introduction and Basics of RL, Defining RL Framework and Markov Decision Process Polices, Value Functions and
Bellman Equations, Exploration vs. Exploitation,Tabular methods and Q-networks,Deep Q-networks,Policy
optimization, Vanilla Policy Gradient Reinforce algorithm and stochastic policy search, Actor-critic methods,
Advanced policy gradient, Model-based RL approach, Meta-learning, Multi-Agent Reinforcement Learning, Partially
Observable Markov Decision Process, Ethics in RL, Applying RL for real-world problems.
References:
1. Reinforcement Learning: An Introduction, Sutton and Barto, 2nd Edition.
2. Reinforcement Learning: State-of-the-Art, Marco Wiering and Martijn van Otterlo, Eds.
3. Artificial Intelligence: A Modern Approach, Stuart J. Russell and Peter Norvig
4. Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville.
Department:
Computer Science and Engineering