Bilevel Entropy based Mechanism Design for Balancing Meta in Video Games

1 code implementation • Autonomous Agents and Multi Agent Systems (AAMAS) 2023 • Sumedh Pendurkar, Chris Chow, Luo Jie, Guni Sharon

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.

A Joint Imitation-Reinforcement Learning Framework for Reduced Baseline Regret

1 code implementation • 20 Sep 2022 • Sheelabhadra Dey, Sumedh Pendurkar, Guni Sharon, Josiah P. Hanna

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.

reinforcement-learning Reinforcement Learning (RL)

The (Un)Scalability of Heuristic Approximators for NP-Hard Search Problems

1 code implementation • 7 Sep 2022 • Sumedh Pendurkar, Taoan Huang, Sven Koenig, Guni Sharon

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.

Combinatorial Optimization