DeepMind has recently released Acme, a library with an objective to simplify the development of reinforcement learning algorithms and agent building blocks. This application can be run at various scales of execution and it is achieved by enabling AI-driven agents to enable simple agent implementations. Acme can be used to create agents with greater parallelization than in previous approaches as per reports. This tool can be used by researchers to reproduce published RL algorithms or rapidly prototype ideas. Acme aims to make the results of various reinforcement learning (RL) algorithms developed in academia and industrial labs easier to reproduce and extend.
Acme strives to bring simple, efficient, and readable agents, that serve both as reference implementations of popular algorithms and as strong baselines, while still providing enough flexibility to do novel research. The design of Acme also attempts to provide multiple points of entry to the RL problem at differing levels of complexity.
Github: https://github.com/deepmind/acme
Paper: https://arxiv.org/pdf/2006.00979.pdf
Installation
To install acme core:
# Install Acme core dependencies.
pip install dm-acme
# Install Reverb, our replay backend.
pip install dm-acme[reverb]
Asif Razzaq is the CEO of Marktechpost, LLC. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over a million monthly views, illustrating its popularity among audiences.
