Reinforcement Learning for Stochastic Networks, Toulouse

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Parallel session: title of session

Keynote

Parallel session: Algorithmic collusion: Foundations for understanding the emergence of anticompetitive behaviour

Parallel session: Reinforcement learning for energy networks

Parallel session: Reinforcement learning for real-life applications

Parallel session: Challenges and progress in statistical reinforcement learning

Opening session

Parallel session: Online learning

Parallel session: Reinforcement learning in continuous time

Parallel session: Learning and optimization

Parallel session: Online learning in stochastic networks

Parallel session: Reinforcement learning in MDPs with large state spaces

Parallel session: Some applications of reinforcement learning to networks

Parallel session: Reinforcement learning and queueing II

Parallel session: Multi-agent systems

Keynote: R. Srikant (University of Illinois Urbana-Champaign)

Keynote: Adam Wierman (Caltech)

Parallel session: Reinforcement learning and queueing I

Parallel session: Reinforcement learning for combinatorial problems

Keynote: Christina Lee Yu (Cornell University)

Keynote: Éric Moulines (École Polytechnique)

Keynote: Bruno Gaujal (INRIA)

Keynote: Vincent François-Lavet (VU, Amsterdam)

Keynote: Emmanuel Rachelson (ISAE-SUPAERO)

Keynote: Sean Meyn (University of Florida)

Keynote: Jim Dai (Cornell University)

Cocktail

Parallel session: Policy gradient methods: optimization and convergence

Parallel session: Reinforcement learning for wireless scheduling

Poster session

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Contributions*

A Deep-Learning Approach to High-Dimensional Impulse Control with Applications to Inventory Management (in session "Parallel session: Reinforcement learning for real-life applications")

A Doubly Robust Approach to Sparse Reinforcement Learning (in session "Parallel session: Reinforcement learning in MDPs with large state spaces")

Achieving Regular and Fair Learning in Combinatorial Multi-Armed Bandit (in session "Parallel session: Reinforcement learning for wireless scheduling")

Artificial Replay: How to get the most out of your data (in session "Parallel session: Learning and optimization")

Backlogged Bandits for Network Utility Maximization (in session "Parallel session: Multi-agent systems")

Boosting Rare Event Simulation in Markov Processes (in session "Poster session")

Computing the bias of stochastic approximation with constant step-size via Stein's method. (in session "Parallel session: Policy gradient methods: optimization and convergence")

Convergence for Natural Policy Gradient on Infinite-State Average-Reward Markov Decision Processes (in session "Parallel session: Policy gradient methods: optimization and convergence")

Decentralized MARL and Preference Learning: Balancing Grid Efficiency and Privacy in Demand Response (in session "Parallel session: Reinforcement learning for energy networks")

Designing M/G/1 Scheduling Policies from Job Size Samples (in session "Parallel session: Reinforcement learning and queueing I")

Dynamic Scheduling and Trajectory Planning for Urban Intersections with Heterogeneous Autonomous Traffic (in session "Poster session")

Dynamic Scheduling of a Multiclass Queue in the Halfin-Whitt Regime: A Computational Approach for High-Dimensional Problems (in session "Parallel session: Reinforcement learning and queueing I")

Energy Management in Smart Grids Using Finite Horizon SOR Q-learning (in session "Parallel session: Some applications of reinforcement learning to networks")

Exploiting Structure In Reinforcement Learning (in session "Keynote: Christina Lee Yu (Cornell University)")

Exploiting Structure in Undiscounted Reinforcement Learning in Markov Decision Processes (in session "Parallel session: Challenges and progress in statistical reinforcement learning")

Exploration in reward machines with near-optimal regret (in session "Parallel session: Challenges and progress in statistical reinforcement learning")

Finite Sample analysis of linear stochastic approximation and TD learning (in session "Keynote: Éric Moulines (École Polytechnique)")

Fleming-Viot particle systems to accelerate optimal policy learning in the presence of costly rare events (in session "Parallel session: Reinforcement learning for combinatorial problems")

Generalization in deep reinforcement learning and the role of representation learning for sequential decision-making tasks (in session "Keynote: Vincent François-Lavet (VU, Amsterdam)")

Inpatient Overflow Management with Proximal Policy Optimization (in session "Keynote: Jim Dai (Cornell University)")

Instability and stability of parameter agnostic policies in parallel server systems (in session "Parallel session: Reinforcement learning and queueing II")

Interpersonal trust: An asymptotic analysis of a stochastic coordination game with multi-agent learning (in session "Parallel session: Multi-agent systems")

Joint learning and scheduling in queueing systems (in session "Parallel session: Reinforcement learning in MDPs with large state spaces")

Learning and Control in Countable State Spaces (in session "Keynote: R. Srikant (University of Illinois Urbana-Champaign)")

Learning Augmented Algorithms for MDPs (in session "Keynote: Adam Wierman (Caltech)")

Learning LP-indices in Average-Reward Restless Multi-Armed Bandits (in session "Parallel session: Some applications of reinforcement learning to networks")

Learning payoffs while routing in skill-based queues (in session "Parallel session: Reinforcement learning and queueing II")

Learning payoffs while routing in skill-based queues (in session "Poster session")

Less than meets the eye: simultaneous experiments as a source of algorithmic seeming collusion (in session "Parallel session: Algorithmic collusion: Foundations for understanding the emergence of anticompetitive behaviour")

Lipschitz Lifelong Reinforcement Learning: transferring value functions across MDPs (in session "Keynote: Emmanuel Rachelson (ISAE-SUPAERO)")

Matching Impatient and Heterogeneous Demand and Supply while Learning (in session "Parallel session: Learning and optimization")

Model-Free Robust $\varphi$-Divergence Reinforcement Learning Using Both Offline and Online Data (in session "Parallel session: Online learning")

Multi-Agent Reinforcement Learning for Collaborative Decision-Making in Network Optimization (in session "Parallel session: Reinforcement learning for wireless scheduling")

Multi-Agent Reinforcement Learning for Power Grid Topology Optimization (in session "Parallel session: Reinforcement learning for energy networks")

Multiagent Q-learning with `satisficing' criteria (in session "Parallel session: Multi-agent systems")

Neural Inventory Control in Networks via Hindsight Differentiable Policy Optimization (in session "Parallel session: Reinforcement learning in MDPs with large state spaces")

Non-preemptive scheduling with non-observable environment (in session "Poster session")

Non-Stationary Gradient Descent for Optimal Auto-Scaling in Serverless Platforms (in session "Parallel session: Reinforcement learning for real-life applications")

On the Global Convergence of Policy Based Methods in Average Reward Problems (in session "Parallel session: Policy gradient methods: optimization and convergence")

Online Learning and Optimization for Queues with Unknown Demand Curve and Service Distribution (in session "Parallel session: Online learning in stochastic networks")

Online Load Balancing and Auto Scaling: Stochastic approximation algorithms for LP-based policy (in session "Parallel session: Reinforcement learning and queueing II")

Optimal congestion signaling under customer heterogeneity with private types (in session "Parallel session: Some applications of reinforcement learning to networks")

Optimized Decision Making via Active Learning of Stochastic Hamiltonians (in session "Parallel session: Reinforcement learning in continuous time")

Provably Efficient Offline Reinforcement Learning in Regular Decision Processes (in session "Parallel session: Challenges and progress in statistical reinforcement learning")

Pseudo-Bayesian Optimization (in session "Parallel session: Learning and optimization")

Quantifying the likelihood of collusion by provably convergent reinforcement learning (in session "Parallel session: Algorithmic collusion: Foundations for understanding the emergence of anticompetitive behaviour")

Recent Advances in Average-Reward Restless Bandits (in session "Parallel session: Online learning in stochastic networks")

Reinforcement learning and regret bounds for admission control (in session "Poster session")

Reinforcement learning in a prisoner's dilemma (in session "Parallel session: Algorithmic collusion: Foundations for understanding the emergence of anticompetitive behaviour")

Scalable Grid Topology Reconfiguration using Consensus-Based Multi Agent Reinforcement Learning (in session "Parallel session: Reinforcement learning for energy networks")

Scalable Learning in Weakly Coupled Markov Decision Processes (in session "Parallel session: Online learning in stochastic networks")

Scalable Policies for the Dynamic Traveling Multi-Maintainer Problem with Alerts (in session "Parallel session: Reinforcement learning for combinatorial problems")

Score-Aware Policy-Gradient Methods and Performance Guarantees using Local Lyapunov Conditions (in session "Parallel session: Online learning")

Simulation Optimization of Reflected Diffusion Processes (in session "Parallel session: Reinforcement learning in continuous time")

Stability and performance of multi-class queueing systems with unknown service rates: A combined scheduling-learning approach (in session "Parallel session: Reinforcement learning and queueing I")

Structured Reinforcement Learning for Delay-Optimal Data Transmission in Dense mmWave Networks (in session "Parallel session: Reinforcement learning for wireless scheduling")

Symphony of experts: orchestration with adversarial insights in reinforcement learning (in session "Parallel session: Reinforcement learning in continuous time")

Tabular and Deep Reinforcement learning for Gittins Index (in session "Parallel session: Online learning")

The Projected Bellman Equation in Reinforcement Learning (in session "Keynote: Sean Meyn (University of Florida)")

The sliding regret (in session "Keynote: Bruno Gaujal (INRIA)")

The Traveling Salesman Problem: Novel Approaches Grounded in Evolutionary Reinforcement Learning (in session "Parallel session: Reinforcement learning for combinatorial problems")

Time-Constrained Robust MDPs (in session "Poster session")

Using Reinforcement and Optimization for grid operation - A solution to the L2PRN 23 competition (in session "Parallel session: Reinforcement learning for real-life applications")

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