Automatic curriculum learning for deep rl: A short survey

R Portelas, C Colas, L Weng, K Hofmann… - arXiv preprint arXiv …, 2020 - arxiv.org
Automatic Curriculum Learning (ACL) has become a cornerstone of recent successes in
Deep Reinforcement Learning (DRL). These methods shape the learning trajectories of …

Beyond games: a systematic review of neural Monte Carlo tree search applications

M Kemmerling, D Lütticke, RH Schmitt - Applied Intelligence, 2024 - Springer
The advent of AlphaGo and its successors marked the beginning of a new paradigm in
playing games using artificial intelligence. This was achieved by combining Monte Carlo …

Towards unifying behavioral and response diversity for open-ended learning in zero-sum games

X Liu, H Jia, Y Wen, Y Hu, Y Chen… - Advances in …, 2021 - proceedings.neurips.cc
Measuring and promoting policy diversity is critical for solving games with strong non-
transitive dynamics where strategic cycles exist, and there is no consistent winner (eg, Rock …

Finding effective security strategies through reinforcement learning and self-play

K Hammar, R Stadler - 2020 16th International Conference on …, 2020 - ieeexplore.ieee.org
We present a method to automatically find security strategies for the use case of intrusion
prevention. Following this method, we model the interaction between an attacker and a …

Asymmetric self-play-enabled intelligent heterogeneous multirobot catching system using deep multiagent reinforcement learning

Y Gao, J Chen, X Chen, C Wang, J Hu… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
Aiming to develop a more robust and intelligent heterogeneous system for adversarial
catching in security and rescue tasks, in this article, we discuss the specialities of applying …

Teachmyagent: a benchmark for automatic curriculum learning in deep rl

C Romac, R Portelas, K Hofmann… - … on Machine Learning, 2021 - proceedings.mlr.press
Training autonomous agents able to generalize to multiple tasks is a key target of Deep
Reinforcement Learning (DRL) research. In parallel to improving DRL algorithms …

Population-based evaluation in repeated rock-paper-scissors as a benchmark for multiagent reinforcement learning

M Lanctot, J Schultz, N Burch, MO Smith… - arXiv preprint arXiv …, 2023 - arxiv.org
Progress in fields of machine learning and adversarial planning has benefited significantly
from benchmark domains, from checkers and the classic UCI data sets to Go and Diplomacy …

Pytag: Challenges and opportunities for reinforcement learning in tabletop games

M Balla, GEM Long, D Jeurissen… - … IEEE Conference on …, 2023 - ieeexplore.ieee.org
In recent years, Game AI research has made important breakthroughs using Reinforcement
Learning (RL). Despite this, RL for modern tabletop games has gained little to no attention …

Optimal Behavior Prior: Data-Efficient Human Models for Improved Human-AI Collaboration

M Yang, M Carroll, A Dragan - arXiv preprint arXiv:2211.01602, 2022 - arxiv.org
AI agents designed to collaborate with people benefit from models that enable them to
anticipate human behavior. However, realistic models tend to require vast amounts of …

Secure Offloading with Adversarial Multi-Agent Reinforcement Learning Against Intelligent Eavesdroppers in UAV-Enabled Mobile Edge Computing

X Li, W Huangfu, X Xu, J Huo… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Mobile edge computing (MEC) has attracted widespread attention due to its ability to
effectively alleviate the cloud computing load and significantly reduce latency. However, the …