Mikayel Samvelyan — accepted AI-conference papers

2024

Craftax: A Lightning-Fast Benchmark for Open-Ended Reinforcement Learning

Michael Matthews, Michael Beukman, Benjamin Ellis, Mikayel Samvelyan, Matthew Thomas Jackson, Samuel Coward +1

ICML 2024spotlight

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 learning fall into one of two categories. Either they are too slow for meaningful research to be performed without enormous…

Cited by 33 Source ↗PDF ↗code ↗Scholar ↗

2024

JaxMARL: Multi-Agent RL Environments and Algorithms in JAX

Alexander Rutherford, Benjamin Ellis, Matteo Gallici, Jonathan Cook, Andrei Lupu, Garðar Ingvarsson +15

NeurIPS 2024poster

Benchmarks are crucial in the development of machine learning algorithms, significantly influencing reinforcement learning (RL) research through the available environments. Traditionally, RL environments run on the CPU, which limits their scalability with the computational resources typically availa…

Cited by 20 Source ↗PDF ↗code ↗Scholar ↗

2024

Rainbow Teaming: Open-Ended Generation of Diverse Adversarial Prompts

Mikayel Samvelyan, Sharath Chandra Raparthy, Andrei Lupu, Eric Hambro, Aram H. Markosyan, Manish Bhatt +6

NeurIPS 2024poster

As large language models (LLMs) become increasingly prevalent across many real-world applications, understanding and enhancing their robustness to adversarial attacks is of paramount importance. Existing methods for identifying adversarial prompts tend to focus on specific domains, lack diversity, o…

Cited by 71 Source ↗PDF ↗Scholar ↗

2023

MAESTRO: Open-Ended Environment Design for Multi-Agent Reinforcement Learning

Mikayel Samvelyan, Akbir Khan, Michael D Dennis, Minqi Jiang, Jack Parker-Holder, Jakob Nicolaus Foerster +2

ICLR 2023poster

Open-ended learning methods that automatically generate a curriculum of increasingly challenging tasks serve as a promising avenue toward generally capable reinforcement learning agents. Existing methods adapt curricula independently over either environment parameters (in single-agent settings) or c…

Cited by 41 Source ↗PDF ↗Scholar ↗

2023

SMACv2: An Improved Benchmark for Cooperative Multi-Agent Reinforcement Learning

Benjamin Ellis, Jonathan Cook, Skander Moalla, Mikayel Samvelyan, Mingfei Sun, Anuj Mahajan +2

NeurIPS 2023poster

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-Agent Challenge (SMAC) has become a popular testbed for centralised training with decentralised execution. However, aft…

Cited by 125 Source ↗PDF ↗code ↗Scholar ↗

2022

Evolving Curricula with Regret-Based Environment Design

Jack Parker-Holder, Minqi Jiang, Michael Dennis, Mikayel Samvelyan, Jakob Foerster, Edward Grefenstette +1

ICML 2022spotlight

Training generally-capable agents with reinforcement learning (RL) remains a significant challenge. A promising avenue for improving the robustness of RL agents is through the use of curricula. One such class of methods frames environment design as a game between a student and a teacher, using regre…

Cited by 148 Source ↗PDF ↗code ↗Scholar ↗

2022

GriddlyJS: A Web IDE for Reinforcement Learning

Christopher Bamford, Minqi Jiang, Mikayel Samvelyan, Tim Rocktäschel

NeurIPS 2022accept

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 typical machine learning researcher. The complexity of environment development has only increased with the rise of procedura…

Cited by 7 Source ↗PDF ↗Scholar ↗

2021

MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research

Mikayel Samvelyan, Robert Kirk, Vitaly Kurin, Jack Parker-Holder, Minqi Jiang, Eric Hambro +4

NeurIPS 2021poster

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 by the community are not explicitly designed for evaluating specific capabilities of RL methods. While there exist enviro…

Cited by 108 Source ↗code ↗Scholar ↗

2021

Tesseract: Tensorised Actors for Multi-Agent Reinforcement Learning

Anuj Mahajan, Mikayel Samvelyan, Lei Mao, Viktor Makoviychuk, Animesh Garg, Jean Kossaifi +3

ICML 2021spotlight

Reinforcement Learning in large action spaces is a challenging problem. This is especially true for cooperative multi-agent reinforcement learning (MARL), which often requires tractable learning while respecting various constraints like communication budget and information about other agents. In thi…

Cited by 45 Source ↗PDF ↗Scholar ↗

2019

MAVEN: Multi-Agent Variational Exploration

Anuj Mahajan, Tabish Rashid, Mikayel Samvelyan, Shimon Whiteson

NeurIPS 2019poster

Centralised training with decentralised execution is an important setting for cooperative deep multi-agent reinforcement learning due to communication constraints during execution and computational tractability in training. In this paper, we analyse value-based methods that are known to have superio…

Cited by 444 Source ↗PDF ↗code ↗Scholar ↗

2018

QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

Tabish Rashid, Mikayel Samvelyan, Christian Schroeder, Gregory Farquhar, Jakob Foerster, Shimon Whiteson

ICML 2018oral

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 centralised fashion in a simulated or laboratory setting, where global state information is available and communication c…

Cited by 3090 Source ↗PDF ↗code ↗Scholar ↗