Multi-agent reinforcement learning: Methods, applications, visionary prospects, and challenges
Multi-agent reinforcement learning (MARL) is a widely used Artificial Intelligence (AI)
technique. However, current studies and applications need to address its scalability, non …
technique. However, current studies and applications need to address its scalability, non …
Efficient adversarial attacks on online multi-agent reinforcement learning
Due to the broad range of applications of multi-agent reinforcement learning (MARL),
understanding the effects of adversarial attacks against MARL model is essential for the safe …
understanding the effects of adversarial attacks against MARL model is essential for the safe …
Multiagent Reinforcement Learning: Methods, Trustworthiness, Applications in Intelligent Vehicles, and Challenges
Multiagent Reinforcement Learning (MARL) plays a pivotal role in intelligent vehicle
systems, offering solutions for complex decision-making, coordination, and adaptive …
systems, offering solutions for complex decision-making, coordination, and adaptive …
Implicit poisoning attacks in two-agent reinforcement learning: Adversarial policies for training-time attacks
In targeted poisoning attacks, an attacker manipulates an agent-environment interaction to
force the agent into adopting a policy of interest, called target policy. Prior work has primarily …
force the agent into adopting a policy of interest, called target policy. Prior work has primarily …
The Bandit Whisperer: Communication Learning for Restless Bandits
Applying Reinforcement Learning (RL) to Restless Multi-Arm Bandits (RMABs) offers a
promising avenue for addressing allocation problems with resource constraints and …
promising avenue for addressing allocation problems with resource constraints and …
Rampart: Reinforcing autonomous multi-agent protection through adversarial resistance in transportation
In the field of multi-agent autonomous transportation, such as automated payload delivery or
highway on-ramp merging, agents routinely exchange knowledge to optimize their shared …
highway on-ramp merging, agents routinely exchange knowledge to optimize their shared …
Corruption-Robust Offline Two-Player Zero-Sum Markov Games
We study data corruption robustness in offline two-player zero-sum Markov games. Given a
dataset of realized trajectories of two players, an adversary is allowed to modify an $\epsilon …
dataset of realized trajectories of two players, an adversary is allowed to modify an $\epsilon …
Towards Offline Opponent Modeling with In-context Learning
Opponent modeling aims at learning the opponent's behaviors, goals, or beliefs to reduce
the uncertainty of the competitive environment and assist decision-making. Existing work …
the uncertainty of the competitive environment and assist decision-making. Existing work …
Data Poisoning to Fake a Nash Equilibria for Markov Games
We characterize offline data poisoning attacks on Multi-Agent Reinforcement Learning
(MARL), where an attacker may change a data set in an attempt to install a (potentially …
(MARL), where an attacker may change a data set in an attempt to install a (potentially …
On faking a Nash equilibrium
We characterize offline data poisoning attacks on Multi-Agent Reinforcement Learning
(MARL), where an attacker may change a data set in an attempt to install a (potentially …
(MARL), where an attacker may change a data set in an attempt to install a (potentially …