Decision Awareness in Reinforcement Learning - End-to-End Optimization Approaches

Explore decision awareness in reinforcement learning through this 54-minute seminar presented by Pierre-Luc Bacon from Université de Montréal. Delve into the end-to-end perspective of optimizing learning systems for optimal decision-making, focusing on recent advances in model-based reinforcement learning. Examine control-oriented transition models using implicit differentiation and the application of neural ordinary differential equations for nonlinear trajectory optimization. Investigate computational challenges and scaling solutions, including efficient Jacobian factorization in forward mode automatic differentiation and novel constrained optimizers inspired by adversarial learning. Cover topics such as optimal model design, smooth Bellman optimality equations, bilevel optimization, continuous-time meta-learning, and gradient flow-based techniques.

Intro
Definition
End-to-End Principle
What is neural network?
Automatic Differentiation
Computational Graph
Reverse and Forward Mode
Control-Oriented Model-Based Reinforcement Learning with Implicit Differentiation
Optimal Model Design Problem (OMD)
Smooth Bellman Optimality Equations
Connection between OMD and Rust (1988)
Bilevel Optimization (Bard 1998)
Implicit and Iterative Differentiation
Benefits under Model Misspecification
Function Approximation and Distractor States
Performance under Model Misspecification
Continuous-Time Meta-Learning with Forward Mode Dif- ferentiation.
Gradient Flow-based Meta-Learning
Time irreversibility
Memory-efficient meta-gradients
Consequence
Empirical Efficiency of COML
Nonlinear Trajectory Optimization
Extragradient Method
Trajectory Optimization with Learned Model