activity
20212026
collaborators
Showing cs.LGShow all

5 papers · 1 filter

cs.LG2026

CODA: Coordination via On-Policy Diffusion for Multi-Agent Offline Reinforcement Learning

Marcel Hedman, Kale-ab Abebe Tessera, Juan Claude Formanek +5

Offline multi-agent reinforcement learning (MARL) enables policy learning from fixed datasets, but is prone to coordination failure: agents trained on static, off-policy data conve…

cs.LG2026

Fairness over Equality: Correcting Social Incentives in Asymmetric Sequential Social Dilemmas

Alper Demir, Hüseyin Aydın, Kale-ab Abebe Tessera +2

Sequential Social Dilemmas (SSDs) provide a key framework for studying how cooperation emerges when individual incentives conflict with collective welfare. In Multi-Agent Reinforce…

cs.LG2025

Remembering the Markov Property in Cooperative MARL

Kale-ab Abebe Tessera, Leonard Hinckeldey, Riccardo Zamboni +2

Cooperative multi-agent reinforcement learning (MARL) is typically formalised as a Decentralised Partially Observable Markov Decision Process (Dec-POMDP), where agents must reason…

cs.LG2024

HyperMARL: Adaptive Hypernetworks for Multi-Agent RL

Kale-ab Abebe Tessera, Arrasy Rahman, Amos Storkey +1

Adaptive cooperation in multi-agent reinforcement learning (MARL) requires policies to express homogeneous, specialised, or mixed behaviours, yet achieving this adaptivity remains…

cs.LG2021

On pseudo-absence generation and machine learning for locust breeding ground prediction in Africa

Ibrahim Salihu Yusuf, Kale-ab Tessera, Thomas Tumiel +4

Desert locust outbreaks threaten the food security of a large part of Africa and have affected the livelihoods of millions of people over the years. Machine learning (ML) has been…