2 citations · 2 across the 10 of their papers we have counts for
11 papers
Benchmarking Open-Ended Multi-Agent Coordination in Language Agents
Kale-ab Abebe Tessera, Andras Szecsenyi, Cameron Barker +7
As language models are increasingly deployed as autonomous agents, they must coordinate with others over long horizons in open-ended interactive tasks. Yet existing evaluations rar…
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…
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…
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…
Redistributing Rewards Across Time and Agents for Multi-Agent Reinforcement Learning
Aditya Kapoor, Kale-ab Tessera, Mayank Baranwal +4
Credit assignmen, disentangling each agent's contribution to a shared reward, is a critical challenge in cooperative multi-agent reinforcement learning (MARL). To be effective, cre…
Agent-Temporal Credit Assignment for Optimal Policy Preservation in Sparse Multi-Agent Reinforcement Learning
Aditya Kapoor, Sushant Swamy, Kale-ab Tessera +4
In multi-agent environments, agents often struggle to learn optimal policies due to sparse or delayed global rewards, particularly in long-horizon tasks where it is challenging to…
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…
Efficiently Quantifying Individual Agent Importance in Cooperative MARL
Omayma Mahjoub, Ruan de Kock, Siddarth Singh +4
Measuring the contribution of individual agents is challenging in cooperative multi-agent reinforcement learning (MARL). In cooperative MARL, team performance is typically inferred…
How much can change in a year? Revisiting Evaluation in Multi-Agent Reinforcement Learning
Siddarth Singh, Omayma Mahjoub, Ruan de Kock +4
Establishing sound experimental standards and rigour is important in any growing field of research. Deep Multi-Agent Reinforcement Learning (MARL) is one such nascent field. Althou…
Selective Reincarnation: Offline-to-Online Multi-Agent Reinforcement Learning
Claude Formanek, Callum Rhys Tilbury, Jonathan Shock +2
'Reincarnation' in reinforcement learning has been proposed as a formalisation of reusing prior computation from past experiments when training an agent in an environment. In this…
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…