Acme: A Research Framework for Distributed Reinforcement Learning
Matthew W. Hoffman, Bobak Shahriari, John Aslanides, Gabriel Barth-Maron, Nikola Momchev, Danila Sinopalnikov, Piotr Stańczyk, Sabela Ramos, Anton Raichuk, Damien Vincent, Léonard Hussenot, Robert Dadashi, Gabriel Dulac-Arnold, Manu Orsini, Alexis Jacq, Johan Ferret, Nino Vieillard, Seyed Kamyar Seyed Ghasemipour, Sertan Girgin, Olivier Pietquin, Feryal Behbahani, Tamara Norman, Abbas Abdolmaleki, Albin Cassirer, Fan Yang, Kate Baumli, Sarah Henderson, Abe Friesen, Ruba Haroun, Alex Novikov, Sergio Gómez Colmenarejo, Serkan Cabi, Caglar Gulcehre, Tom Le Paine, Srivatsan Srinivasan, Andrew Cowie, Ziyu Wang, Bilal Piot, Nando de Freitas
THE PROBLEM
This paper focuses on Imitation & Reinforcement LearningReinforcement Learning (RL)Teaching a robot through trial and error using rewards.. Acme is a production-grade framework that lets you build complex distributed Imitation & Reinforcement LearningReinforcement Learning (RL)Teaching a robot through trial and error using rewards. agents from simple, reusable modules—no need to rewrite the whole Robot LearningTrainingThe process of fitting a model using data or experience. pipeline for each algorithm. If you're implementing robotics Imitation & Reinforcement LearningReinforcement Learning (RL)Teaching a robot through trial and error using rewards. systems at scale (multi-agent, distributed compute), this saves months of engineering and makes your code maintainable and reproducible. Read the paper by tracking the Core ConceptsTaskThe job the robot is supposed to complete, such as pick-and-place, navigation, or drawer opening. definition, the Core ConceptsRobotA physical system with sensors and actuators that can observe the world and take actions. or data assumptions, and the evidence that supports the claimed improvement.
HOW IT WORKS
Task framing
Core method
Data and supervision
Evaluation evidence
KEY RESULTS
Acme is a production-grade framework that lets you build complex distributed Imitation & Reinforcement LearningReinforcement Learning (RL)Teaching a robot through trial and error using rewards. agents from simple, reusable modules—no need to rewrite the whole Robot LearningTrainingThe process of fitting a model using data or experience. pipeline for each algorithm. If you're implementing robotics Imitation & Reinforcement LearningReinforcement Learning (RL)Teaching a robot through trial and error using rewards. systems at scale (multi-agent, distributed compute), this saves months of engineering and makes your code maintainable and reproducible.
WHY DEVELOPERS SHOULD CARE
Acme is a production-grade framework that lets you build complex distributed Imitation & Reinforcement LearningReinforcement Learning (RL)Teaching a robot through trial and error using rewards. agents from simple, reusable modules—no need to rewrite the whole Robot LearningTrainingThe process of fitting a model using data or experience. pipeline for each algorithm. If you're implementing robotics Imitation & Reinforcement LearningReinforcement Learning (RL)Teaching a robot through trial and error using rewards. systems at scale (multi-agent, distributed compute), this saves months of engineering and makes your code maintainable and reproducible.
LIMITATIONS
The main limitation to check is whether the claimed behavior holds outside the paper's reported setup. That means testing across different Core ConceptsRobotA physical system with sensors and actuators that can observe the world and take actions. embodiments, scenes, objects, and data distributions.
WHAT COMES NEXT
The practical next step is independent reproduction with clear baselines, ablations, and stress tests. For a developer, the useful follow-up is to map the paper's Imitation & Reinforcement LearningReinforcement Learning (RL)Teaching a robot through trial and error using rewards. assumptions onto a concrete Core ConceptsRobotA physical system with sensors and actuators that can observe the world and take actions. stack, then test the smallest version of the method that could run end to end.