Reinforcement learning is an area of machine learning, where an agent or a system of agents learn to archive a goal by interacting with their environment. RL is often seen as the third area of machine learning, in addition to supervised and unsupervised areas, in which learning of an agent occurs as a result of its own actions and interaction with the environment.
In recent years there has been success in reinforcement learning research in both theoretical and applied fields. It was applied in a variety of fields such as robotics, pattern recognition, personalized medical treatment, drug discovery, speech recognition, computer vision, and natural language processing. This course primarily focuses on training students to frame reinforcement learning problems and to tackle algorithms from dynamic programming, Monte Carlo and temporal-difference learning. Students will progress towards larger state space environments using function approximation, deep Q-networks and state-of-the-art policy gradient algorithms. We will also go over the recent methods that are based on reinforcement learning, such as imitation learning, meta learning and more complex environment formulations.
- CSE 4/546: CSE4/574 or CSE4/555 or CSE4/573 or CSE4/568 is recommended to be either completed or taken during the same semester.
|Alina Vereshchaka (Instructor)||avereshc[at]buffalo.edu||Mon, Wed 12:30-2pm & by appointments|
|Anantha Srinath Sedimbi (Friend of the Course)||firstname.lastname@example.org||Fri 4-5pm & by appointments|
|Srisai Karthik Neelamraju (Friend of the Course)||email@example.com||By appointments|
Selected Final Projects Presentations
- Multi-agent Reinforcemet Learning Nitin Kulkarni, Sai Reddy, Sehaj Grover
- Comparison of RL Algorithms Velivela Vamsi Krishna, Sudhir Yarram
- MAgent RL Tyler Perison
- AWS Deep Racer Upmanyu Tyagi
- MARL with Neural Fictitious Self Play Nikhil Vasudeva
- Reinforcement Learning for Image Captioning Pengyu Yan
- Soft Actor-Critic Agent in MineRL Jacob Santoni, Liam Orr, Rohith Reddy
- Policy Gradient and Deep Q Networks on Openai Environments Joseph Distefano
- Solving Several Multi-Agent RL Sougata Saha, Zebin Li
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