Research
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Emergent Cooperation from Mutual Acknowledgment Exchange in Multi-Agent Reinforcement Learning
Peer incentivization (PI) is a recent approach where all agents learn to reward or penalize each other in a distributed fashion, which often leads to emergent cooperation. Current PI mechanisms implicitly assume a flawless communication channel in order to exchange...
CROP: Towards Distributional-Shift Robust Reinforcement Learning using Compact Reshaped Observation Processing
The safe application of reinforcement learning (RL) requires generalization from limited training data to unseen scenarios. Yet, fulfilling tasks under changing circumstances is a key challenge in RL. Current state-of-the-art approaches for generalization apply data augmentation techniques to increase the...
Attention-Based Recurrence for Multi-Agent Reinforcement Learning under Stochastic Partial Observability
Stochastic partial observability poses a major challenge for decentralized coordination in multi-agent reinforcement learning but is largely neglected in state-of-the-art research due to a strong focus on state-based centralized training for decentralized execution (CTDE) and benchmarks that lack sufficient stochasticity...
Towards Anomaly Detection in Reinforcement Learning
Identifying datapoints that substantially differ from normality is the task of anomaly detection (AD). While AD has gained widespread attention in rich data domains such as images, videos, audio and text, it has has been studied less frequently in the...
Emergent Cooperation from Mutual Acknowledgment Exchange
Peer incentivization (PI) is a recent approach, where all agents learn to reward or to penalize each other in a distributed fashion which often leads to emergent cooperation. Current PI mechanisms implicitly assume a flawless communication channel in order to...
VAST: Value Function Factorization with Variable Agent Sub-Teams
Value function factorization (VFF) is a popular approach to cooperative multi-agent reinforcement learning in order to learn local value functions from global rewards. However, state-of-the-art VFF is limited to a handful of agents in most domains. We hypothesize that this...
Stochastic Market Games
Some of the most relevant future applications of multi-agent systems like autonomous driving or factories as a service display mixed-motive scenarios, where agents might have conflicting goals. In these settings agents are likely to learn undesirable outcomes in terms of...
Resilient Multi-Agent Reinforcement Learning with Adversarial Value Decomposition
We focus on resilience in cooperative multi-agent systems, where agents can change their behavior due to udpates or failures of hardware and software components. Current state- of-the-art approaches to cooperative multi-agent reinforcement learning (MARL) have either focused on idealized settings...
Learning and Testing Resilience in Cooperative Multi-Agent Systems
State-of-the-art multi-agent reinforcement learning has achieved remarkable success in recent years. The success has been mainly based on the assumption that all teammates perfectly cooperate to optimize a global objective in order to achieve a common goal. While this may...