| Course ID: | CSCI(ARTI) 4600/6600. 3 hours. |
| Course Title: | Reinforcement Learning |
Course
Description: | Reinforcement learning studies methods for learning to act optimally based on the reward or punishment over time. Such machine learning is useful when we wish to learn high-quality behavior under uncertainty and the only data are reward signals. Introduces classical and modern methods in single- and multi-agent settings. |
| Oasis Title: | Reinforcement Learning |
| Prerequisite: | CSCI(PHIL) 4550/6550 |
Semester Course
Offered: | Not offered on a regular basis. |
| Grading System: | A-F (Traditional) |
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| Course Objectives: | 1. Situate and understand a key area of artificial intelligence and specifically in the field of machine learning. Understand the corresponding class of problems.
2. Study the challenges and algorithms for reinforcement learning by agents situated in uncertain single-agent and multi-agent environments.
3. Gain proficiency in the use of computing tools related to reinforcement learning, designing and giving effective research presentations, and working in a team. |
| Topical Outline: | I. Introduction
a. Requirements for reinforcement learning (RL) and its limitations, exploration vs. exploitation
b. Probability theory background
c. History of RL
II. Model-based RL
a. Markov decision processes (MDP)
b. Planning using dynamic programming
c. Model learning (CE, Dyna, prioritized sweeping, RTDP*)
III. Model-free RL
a. Value-based learning
- On-policy methods (Sarsa, TD, eligibility traces)
- Off-policy methods (Q-learning, Deep Q networks)
b. Policy-based learning
- Policy gradient methods (Monte Carlo, Trust-region, Proximal policy)
- Actor-Critic Schema (A2C, A3C)
IV. Advanced Concepts in RL
a. Inverse RL
b. Multi-agent RL (multi-agent AC, MADDPG, LOLA)
c. Human RL (time permitting) |