Deep Reinforcement Learning

This course will cover the breadth of modern model-free RL methods, discuss their limitations and introduce a variety of current research topics. In particular, we expect to cover the following:

• Deep learning methodology and architectures

• Stabilization of approximated value estimation

• Modern actor-critic methods

• Planning as inference

• Exploration with deep networks

• Offline reinforcement learning

• Deep multi-agent reinforcement learning

• Multi-task and sim2real learning