CS 391 - Special Topic: Machine Learning
Syllabus
[From http://www-anw.cs.umass.edu/~rich/book/the-book.html:]
Part I: The Problem
1
Introduction
1.1 Reinforcement Learning
1.2 Examples
1.3 Elements of Reinforcement Learning
1.4 An Extended Example: Tic-Tac-Toe
1.5 Summary
1.6 History of Reinforcement Learning
1.7 Bibliographical Remarks
2
Evaluative Feedback
2.1 An n-armed Bandit Problem
2.2 Action-Value Methods
2.3 Softmax Action Selection
2.4 Evaluation versus Instruction
2.5 Incremental Implementation
2.6 Tracking a Nonstationary Problem
2.7 Optimistic Initial Values
2.8 Reinforcement Comparison
2.9 Pursuit Methods
2.10 Associative Search
2.11 Conclusion
2.12 Bibliographical and Historical Remarks
3
The Reinforcement Learning Problem
3.1 The Agent-Environment Interface
3.2 Goals and Rewards
3.3 Returns
3.4 A Unified Notation for Episodic and Continual Tasks
3.5 The Markov Property
3.6 Markov Decision Processes
3.7 Value Functions
3.8 Optimal Value Functions
3.9 Optimality and Approximation
3.10 Summary
3.11 Bibliographical and Historical Remarks
Part II: Elementary Methods
4
Dynamic Programming
4.1 Policy Evaluation
4.2 Policy Improvement
4.3 Policy Iteration
4.4 Value Iteration
4.5 Asynchronous Dynamic Programming
4.6 Generalized Policy Iteration
4.7 Efficiency of Dynamic Programming
4.8 Summary
4.9 Historical and Bibliographical Remarks
5
Monte Carlo Methods
5.1 Monte Carlo Policy Evaluation
5.2 Monte Carlo Estimation of Action Values
5.3 Monte Carlo Control
5.4 On-Policy Monte Carlo Control
5.5 Evaluating One Policy While Following Another
5.6 Off-Policy Monte Carlo Control
5.7 Incremental Implementation
5.8 Summary
5.9 Historical and Bibliographical Remarks
6
Temporal Difference Learning
6.1 TD Prediction
6.2 Advantages of TD Prediction Methods
6.3 Optimality of TD(0)
6.4 Sarsa: On-Policy TD Control
6.5 Q-learning: Off-Policy TD Control
6.6 Actor-Critic Methods (*)
6.7 R-Learning for Undiscounted Continual Tasks (*)
6.8 Games, After States, and other Special Cases
6.9 Conclusions
6.10 Historical and Bibliographical Remarks
Part III: A Unified View
7
Eligibility Traces
7.1 n-step TD Prediction
7.2 The Forward View of TD()
7.3 The Backward View of TD()
7.4 Equivalence of the Forward and Backward Views
7.5 Sarsa()
7.6 Q()
7.7 Eligibility Traces for Actor-Critic Methods (*)
7.8 Replacing Traces
7.9 Implementation Issues
7.10 Variable (*)
7.11 Conclusions
7.12 Bibliographical and Historical Remarks
8
Generalization and Function Approximation
8.1 Value Prediction with Function Approximation
8.2 Gradient-Descent Methods
8.3 Linear Methods
8.3.1 Coarse Coding
8.3.2 Tile Coding
8.3.3 Radial Basis Functions
8.3.4 Kanerva Coding
8.4 Control with Function Approximation
8.5 Off-Policy Bootstrapping
8.6 Should We Bootstrap?
8.7 Summary
8.8 Bibliographical and Historical Remarks
