Here you will find out about:
- foundations of RL methods: value/policy iteration, q-learning, policy gradient, etc.
--- with math & batteries included
- using deep neural networks for RL tasks
--- also known as "the hype train"
- state of the art RL algorithms
--- and how to apply duct tape to them for practical problems.
- and, of course, teaching your neural network to play games
--- because that's what everyone thinks RL is about. We'll also use it for seq2seq and contextual bandits.
Jump in. It's gonna be fun!
Do you have technical problems? Write to us: [email protected]