The Challenges of Model-Based Reinforcement Learning and How to Overcome Them - Csaba Szepesvári

Explore the complexities and solutions in model-based reinforcement learning through this comprehensive lecture by Csaba Szepesvári at the Institute for Advanced Study. Delve into key concepts such as Markov Decision Processes, online reinforcement learning, and the integration of neural networks. Examine the benefits and challenges of model-based approaches, including modeling errors and the curse of dimensionality. Learn about efficient planning techniques, function approximation, and methods for computing optimal policies. Gain insights into the practical applications and limitations of these advanced machine learning strategies through examples and in-depth discussions.

Intro
Premise
Collaborators
Context
Markov Decision Processes
Environment
Simulation
Experience
Online reinforcement learning
Reinforcement learning and neural networks
Questions
Example
Benefits
Reality check
Challenges
Modeling errors
Question
Efficient planning
Defining things
Expected reward
The curse of dimensions
Computing the optimal policy
Planning
Function Approximation