# Reinforcement Learning and Geometric Algorithms

## CMPS 499/CSCE 572

### General Information

**Instructor**

Miao Jin Office hours: TR 9:30am - 11:00am ACTR 237

**Times & Places**

TR 11:00am - 12:15pm, ACTR 113

**Objectives:**

Reinforcement Learning: a range of topics related to reinforcement learning.

Topics include:
- Markov decision processes
- Dynamic programming
- Monte Carlo learning
- Temporal difference learning
- Value function approximation
- Policy gradient
- Integration of learning and planning

Geometric Algorithms: a range of topics related to geometric representation
and computation. We will study fundamental data structures and algorithms from computational geometry and their applications to problems that occur in practice.

Topics may include:
- Convex hulls
- Line segment intersection
- Triangulating a polygon
- Enclosing circle
- Voronoi diagrams
- Delaunay Triangulations
- Robot Motion Planning

**Prerequisites**

CMPS 341 Formal Foundations of Computer Science.

**Textbooks**

An Introduction to Reinforcement Learning, Second edition, in progress (Available free online! ) by Richard S. Sutton and Andrew G. Barto (optional).

Computational Geometry, Algorithms and Applications by Mark de Berg et al
(optional).

**Grading**

- 50% Reinforcement Learning Project
- 25% Midterm exam (Reinforcement Learning)
- 25% Final exam (Geometric Algorithms)