http://rail.eecs.berkeley.edu/deeprlcourse/

Lectures are recorded and live streamed

Material which will be covered:

1. From supervised learning to decision making

2. Model-free algorithms: Q-learning, policy gradients, actor-critic

3. Advanced model learning and prediction

4. Transfer and multi-task learning, meta-learning

5. Exploration

6. Open problems, research talks, invited lectures

There’s a subreddit for this course: r/berkeleydeeprlcourse