University at Buffalo 3 credits Computer Science & Engineering
“This advanced course is for students who have a foundation of reinforcement learning (RL) methods and have an interest in exploring the area of multi-agent systems. This course focuses primarily on the application of advanced algorithms in RL to solve multiagent systems and real-world tasks. We will cover the foundations of formulation and solving tasks based on the multi agent interactions. We are planning to apply a range of methods to solve the tasks, ranging from the tabular methods to the cutting-edge deep-RL algorithms, like Soft-Actor Critics (SAC) and Deep Deterministic Policy Gradients (DDPG). We will apply these concepts to train agents to walk, drive, or perform other complex tasks in the multi-agent scenarios. We will also discuss issues associated with scaling and deployment multi-agent systems as well as ethical and safety concerns.
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