Reinforcement Learning for Stochastic Networks, Toulouse

Session

Parallel session: Reinforcement learning in continuous time

Jun 17, 2024, 3:30 PM

A001 (ENSEEIHT)

Description

Organizer and chair: Harsha Honnappa

28. Optimized Decision Making via Active Learning of Stochastic Hamiltonians

Dr Chandrajit Bajaj (UT Austin)

6/17/24, 3:30 PM

A Hamiltonian represents the energy of a dynamical system in phase space with coordinates of position and momentum. The Hamilton’s equations of motion are obtainable as coupled symplectic differential equations. In this talk I shall show how optimized decision making (action sequences) can be obtained via a reinforcement learning problem wherein the agent interacts with the unknown environment...

51. Simulation Optimization of Reflected Diffusion Processes

Harsha Honnappa (Purdue University)

6/17/24, 4:00 PM

Recent work by Ata, Harrison and Si (2023) introduced a simulation-based computational method for stochastic optimal drift control of multidimensional reflected Brownian motion (RBM). The main objective of their work is to compute an optimal “closed loop” stationary Markov control policy. In this talk, I will present our recent results on computing optimal “open loop” controls for finite...

75. Symphony of experts: orchestration with adversarial insights in reinforcement learning

Chiara Mignacco (Université Paris-Saclay)

6/17/24, 4:30 PM

Structured reinforcement learning leverages policies with advantageous properties to reach better performance, particularly in scenarios where exploration poses challenges. We explore this field through the concept of orchestration, where a (small) set of expert policies guides decision-making; the modeling thereof constitutes our first contribution. We then establish value-functions regret...