Automatic curriculum learning for deep rl: A short survey
Automatic Curriculum Learning (ACL) has become a cornerstone of recent successes in
Deep Reinforcement Learning (DRL). These methods shape the learning trajectories of …
Deep Reinforcement Learning (DRL). These methods shape the learning trajectories of …
Beyond games: a systematic review of neural Monte Carlo tree search applications
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 …
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
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 …
transitive dynamics where strategic cycles exist, and there is no consistent winner (eg, Rock …
Finding effective security strategies through reinforcement learning and self-play
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 …
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
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 …
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
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 …
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
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 …
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
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 …
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
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 …
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 …
effectively alleviate the cloud computing load and significantly reduce latency. However, the …