Acme: Simplifying AI Development
The high-level goals of Acme are to enable reproducibility, simplify algorithm design, and enhance the readability of RL agents. These goals are important in understanding the challenges of RL problems, designing new algorithms, and translating research papers into code.
Acme bridges the gap between large-, medium-, and small-scale experiments by providing a classical RL interface. This interface connects an actor (action-selecting agent) to an environment and allows for actions selection, observation, and updating. Internally, Acme splits the problem into “acting” and “learning from data” components, making it easier to reuse the acting portions across different agents and parallelize the learning process.
Through the use of Acme, developers can create, test, and debug agents in small-scale scenarios before scaling them up. Acme also provides useful utilities like checkpointing and computational helpers to simplify RL algorithms.
To enable its design, Acme utilizes Reverb, a data storage system built for machine learning and reinforcement learning. Reverb supports experience replay and other data structure representations, making it seamless to use for on- and off-policy algorithms.
In addition to the infrastructure, Acme offers single-process implementations of various agents for continuous control, discrete Q-learning, and more. These agents can easily be scaled for distributed processing. Acme’s performance has been benchmarked in environments like the control suite, Atari, and bsuite.
To get a better understanding of Acme and its capabilities, you can check out the videos of agents trained using this framework. Additional results and details can also be found in the accompanying paper. If you’re interested in simplifying your own agents, you can explore the Acme GitHub repository.
In conclusion, Acme is a powerful tool for simplifying AI development. It allows for reproducibility, easier algorithm design, and enhanced readability. With its seamless integration with Reverb and various benchmarked agents, Acme is a valuable resource for developers in the RL field.