59 citations · 118 across the 53 of their papers we have counts for
15 papers · 1 filter
Mirror Mode in Fire Emblem: Beating Players at their own Game with Imitation and Reinforcement Learning
Yanna Elizabeth Smid, Peter van der Putten, Aske Plaat
Enemy strategies in turn-based games should be surprising and unpredictable. This study introduces Mirror Mode, a new game mode where the enemy AI mimics the personal strategy of a…
Guiding Skill Discovery with Foundation Models
Zhao Yang, Thomas M. Moerland, Mike Preuss +3
Learning diverse skills without hand-crafted reward functions could accelerate reinforcement learning in downstream tasks. However, existing skill discovery methods focus solely on…
A Benchmark Study of Deep Reinforcement Learning Algorithms for the Container Stowage Planning Problem
Yunqi Huang, Nishith Chennakeshava, Alexis Carras +4
Container stowage planning (CSPP) is a critical component of maritime transportation and terminal operations, directly affecting supply chain efficiency. Owing to its complexity, C…
A Unified Framework for Zero-Shot Reinforcement Learning
Jacopo Di Ventura, Jan Felix Kleuker, Aske Plaat +1
Zero-shot reinforcement learning (RL) has emerged as a setting for developing general agents, capable of solving downstream tasks without additional training or planning at test-ti…
Towards a Practical Understanding of Lagrangian Methods in Safe Reinforcement Learning
Lindsay Spoor, Álvaro Serra-Gómez, Aske Plaat +1
Safe reinforcement learning addresses constrained optimization problems where maximizing performance must be balanced against safety constraints, and Lagrangian methods are a widel…
Analysis of Bluffing by DQN and CFR in Leduc Hold'em Poker
Tarik Zaciragic, Aske Plaat, K. Joost Batenburg
In the game of poker, being unpredictable, or bluffing, is an essential skill. When humans play poker, they bluff. However, most works on computer-poker focus on performance metric…