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Found 8 papers, 5 papers with code
Bilevel Entropy based Mechanism Design for Balancing Meta in Video Games
We address a mechanism design problem where the goal of the designer is to maximize the entropy of a player’s mixed strategy at a Nash equilibrium.
Task Phasing: Automated Curriculum Learning from Demonstrations
Applying reinforcement learning (RL) to sparse reward domains is notoriously challenging due to insufficient guiding signals.
A Joint Imitation-Reinforcement Learning Framework for Reduced Baseline Regret
The learning process in JIRL assumes the availability of a baseline policy and is designed with two objectives in mind \textbf{(a)} leveraging the baseline's online demonstrations to minimize the regret w. r. t the baseline policy during training, and \textbf{(b)} eventually surpassing the baseline performance.
The (Un)Scalability of Heuristic Approximators for NP-Hard Search Problems
Our first experimental results for three representative NP-hard minimum-cost path problems suggest that using neural networks to approximate completely informed heuristic functions with high precision might result in network sizes that scale exponentially in the instance sizes.
Technical Report: Hybrid Autonomous Intersection Management
This document provides technical details regarding the Hybrid-AIM simulator that was used in Sharon and Stone (2017) and Parks-Young and Sharon (2022).
Learning an Interpretable Traffic Signal Control Policy
Given such a safety-critical domain, the affiliated actuation policy is required to be interpretable in a way that can be understood and regulated by a human.
Traffic Optimization For a Mixture of Self-interested and Compliant Agents
This paper focuses on two commonly used path assignment policies for agents traversing a congested network: self-interested routing, and system-optimum routing.
Overview: Generalizations of Multi-Agent Path Finding to Real-World Scenarios
Multi-agent path finding (MAPF) is well-studied in artificial intelligence, robotics, theoretical computer science and operations research.
