CS394R: Reinforcement Learning: Theory and Practice -- Fall 2007: Assignments Page
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### Week 0 (8/30): Class Overview

### Week 1 (9/4,9/6): Introduction

### Week 2 (9/11,9/13): Evaluative Feedback

### Week 3 (9/18,20): The Reinforcement Learning Problem

### Week 4 (9/25,27): Dynamic Programming

### Week 5 (10/2,4): Monte Carlo Methods

### Week 6 (10/9,11): Temporal Difference Learning

### Week 7 (10/16,18): Eligibility Traces

### Week 8 (10/23,25): Generalization and Function Approximation

### Week 9 (10/30,11,1): Planning and Learning

Chapter 9 of the textbook

### Week 10 (11/6,8): Case Studies

Chapters 10 and 11 of the textbook

### Week 11 (11/13,15): Abstraction: Options and Hierarchy

**Between MDPs and semi-MDPs: A Framework for Temporal Abstraction in Reinforcement Learning**.

Sutton, R.S., Precup, D., Singh, S.

Artificial Intelligence 112:181-211, 1999.

Due **Tuesday**.
**The MAXQ Method for Hierarchical Reinforcement Learning**.

Thomas G. Dietterich

Proceedings of the 15th International Conference on Machine Learning, 1998.

Due **Thursday**.

### Week 12 (11/20): Helicopter Control and Robot Soccer

Autonomous helicopter flight via reinforcement learning.

Andrew Ng, H. Jin Kim, Michael Jordan and Shankar Sastry.

In S. Thrun, L. Saul, and B. Schoelkopf (Eds.), Advances in Neural Information Processing Systems (NIPS) 17, 2004.

Due **Tuesday**.
Making a Robot Learn to Play Soccer Using Reward and Punishment.

Heiko Müller, Martin Lauer, Roland Hafner, Sascha Lange, Artur Merke and Martin Riedmiller.

Due **Tuesday**.
Note that both papers are due on Tuesday!

### Week 13 (11/27,29): Adaptive Representations and Transfer Learning

Evolutionary Function Approximation for Reinforcement Learning.

Shimon Whiteson and Peter Stone.

Journal of Machine Learning Research, 7(May):877-917, 2006.

Due **Tuesday**.
Value Functions for RL-Based Behavior Transfer: A Comparative Study.

Matthew Taylor, Peter Stone and Yaxin Liu.

In Proceedings of the Twentieth National Conference on Artificial Intelligence, July 2005.

Due **Thursday**.

### Week 14 (12/4,6): Advice and Multiagent Reinforcement Learning

Creating Advice-Taking Reinforcement Learners.

Richard Maclin and Jude Shavlik.

Machine Learning, 22, pp. 251-281, 1996.

Due **Tuesday**.
Multi-Agent Reinforcement Learning: Independent vs. Cooperative Agents.

Ming Tan.

In Proceedings of the Tenth International Conference on Machine Learning (ICML-93), pages 330-337, 1993.

Due **Thursday**.
**Final Project: due at 12:30pm on Thursday, 12/6 **

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