Introduction to Deep RL

Machine Learning and AI Academy

This is the first lecture in a series of lectures that will detail deep reinforcement learning. We also provide background knowledge needed for understanding the material, including but not limited to, probability theory and optimisation in further courses and videos to come. At the end of the course, you will able to use and implement deep on your specific problem domain. We cover:
1. Model-Free RL (e.g., DQN, PPO, TRPO)
2. Model-Based RL (e.g., PILCO , Planet)

Slides can be found on: https://docs.google.com/presentation/d/1Y4H8VBpNm9NXvMc1JHUt4atSLpIWvocCPVOsdf2ffvY/edit?usp=sharing

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