428 citations · 938 across the 23 of their papers we have counts for
25 papers
Robust Agents in Open-Ended Worlds
Mikayel Samvelyan
The growing prevalence of artificial intelligence (AI) in various applications underscores the need for agents that can successfully navigate and adapt to an ever-changing, open-en…
SIRAJ: Diverse and Efficient Red-Teaming for LLM Agents via Distilled Structured Reasoning
Kaiwen Zhou, Ahmed Elgohary, A S M Iftekhar +1
The ability of LLM agents to plan and invoke tools exposes them to new safety risks, making a comprehensive red-teaming system crucial for discovering vulnerabilities and ensuring…
SPARQ: Synthetic Problem Generation for Reasoning via Quality-Diversity Algorithms
Alex Havrilla, Edward Hughes, Mikayel Samvelyan +1
Large language model (LLM) driven synthetic data generation has emerged as a powerful method for improving model reasoning capabilities. However, most methods either distill large…
Dialect Normalization using Large Language Models and Morphological Rules
Antonios Dimakis, John Pavlopoulos, Antonios Anastasopoulos
Natural language understanding systems struggle with low-resource languages, including many dialects of high-resource ones. Dialect-to-standard normalization attempts to tackle thi…
The Llama 3 Herd of Models
Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri +556
Modern artificial intelligence (AI) systems are powered by foundation models. This paper presents a new set of foundation models, called Llama 3. It is a herd of language models th…
Rainbow Teaming: Open-Ended Generation of Diverse Adversarial Prompts
Mikayel Samvelyan, Sharath Chandra Raparthy, Andrei Lupu +9
As large language models (LLMs) become increasingly prevalent across many real-world applications, understanding and enhancing their robustness to adversarial attacks is of paramou…
Craftax: A Lightning-Fast Benchmark for Open-Ended Reinforcement Learning
Michael Matthews, Michael Beukman, Benjamin Ellis +4
Benchmarks play a crucial role in the development and analysis of reinforcement learning (RL) algorithms. We identify that existing benchmarks used for research into open-ended lea…
Multi-Agent Diagnostics for Robustness via Illuminated Diversity
Mikayel Samvelyan, Davide Paglieri, Minqi Jiang +2
In the rapidly advancing field of multi-agent systems, ensuring robustness in unfamiliar and adversarial settings is crucial. Notwithstanding their outstanding performance in famil…
Mix-ME: Quality-Diversity for Multi-Agent Learning
Garðar Ingvarsson, Mikayel Samvelyan, Bryan Lim +3
In many real-world systems, such as adaptive robotics, achieving a single, optimised solution may be insufficient. Instead, a diverse set of high-performing solutions is often requ…
JaxMARL: Multi-Agent RL Environments and Algorithms in JAX
Alexander Rutherford, Benjamin Ellis, Matteo Gallici +18
Benchmarks are crucial in the development of machine learning algorithms, with available environments significantly influencing reinforcement learning (RL) research. Traditionally,…
MAESTRO: Open-Ended Environment Design for Multi-Agent Reinforcement Learning
Mikayel Samvelyan, Akbir Khan, Michael Dennis +5
Open-ended learning methods that automatically generate a curriculum of increasingly challenging tasks serve as a promising avenue toward generally capable reinforcement learning a…
SMACv2: An Improved Benchmark for Cooperative Multi-Agent Reinforcement Learning
Benjamin Ellis, Jonathan Cook, Skander Moalla +5
The availability of challenging benchmarks has played a key role in the recent progress of machine learning. In cooperative multi-agent reinforcement learning, the StarCraft Multi-…
Hierarchical Kickstarting for Skill Transfer in Reinforcement Learning
Michael Matthews, Mikayel Samvelyan, Jack Parker-Holder +2
Practising and honing skills forms a fundamental component of how humans learn, yet artificial agents are rarely specifically trained to perform them. Instead, they are usually tra…
GriddlyJS: A Web IDE for Reinforcement Learning
Christopher Bamford, Minqi Jiang, Mikayel Samvelyan +1
Progress in reinforcement learning (RL) research is often driven by the design of new, challenging environments -- a costly undertaking requiring skills orthogonal to that of a typ…
Insights From the NeurIPS 2021 NetHack Challenge
Eric Hambro, Sharada Mohanty, Dmitrii Babaev +26
In this report, we summarize the takeaways from the first NeurIPS 2021 NetHack Challenge. Participants were tasked with developing a program or agent that can win (i.e., 'ascend' i…
Evolving Curricula with Regret-Based Environment Design
Jack Parker-Holder, Minqi Jiang, Michael Dennis +4
It remains a significant challenge to train generally capable agents with reinforcement learning (RL). A promising avenue for improving the robustness of RL agents is through the u…
Generalization in Cooperative Multi-Agent Systems
Anuj Mahajan, Mikayel Samvelyan, Tarun Gupta +4
Collective intelligence is a fundamental trait shared by several species of living organisms. It has allowed them to thrive in the diverse environmental conditions that exist on ou…
Reinforcement Learning in Factored Action Spaces using Tensor Decompositions
Anuj Mahajan, Mikayel Samvelyan, Lei Mao +6
We present an extended abstract for the previously published work TESSERACT [Mahajan et al., 2021], which proposes a novel solution for Reinforcement Learning (RL) in large, factor…
MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research
Mikayel Samvelyan, Robert Kirk, Vitaly Kurin +7
Progress in deep reinforcement learning (RL) is heavily driven by the availability of challenging benchmarks used for training agents. However, benchmarks that are widely adopted b…
Tesseract: Tensorised Actors for Multi-Agent Reinforcement Learning
Anuj Mahajan, Mikayel Samvelyan, Lei Mao +6
Reinforcement Learning in large action spaces is a challenging problem. Cooperative multi-agent reinforcement learning (MARL) exacerbates matters by imposing various constraints on…
Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
Tabish Rashid, Mikayel Samvelyan, Christian Schroeder de Witt +3
In many real-world settings, a team of agents must coordinate its behaviour while acting in a decentralised fashion. At the same time, it is often possible to train the agents in a…
MAVEN: Multi-Agent Variational Exploration
Anuj Mahajan, Tabish Rashid, Mikayel Samvelyan +1
Centralised training with decentralised execution is an important setting for cooperative deep multi-agent reinforcement learning due to communication constraints during execution…
The StarCraft Multi-Agent Challenge
Mikayel Samvelyan, Tabish Rashid, Christian Schroeder de Witt +7
In the last few years, deep multi-agent reinforcement learning (RL) has become a highly active area of research. A particularly challenging class of problems in this area is partia…
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
Tabish Rashid, Mikayel Samvelyan, Christian Schroeder de Witt +3
In many real-world settings, a team of agents must coordinate their behaviour while acting in a decentralised way. At the same time, it is often possible to train the agents in a c…