Acme: A research framework for reinforcement learning
Acme is a library of reinforcement learning (RL) agents and agent building blocks. Acme strives to expose 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.
At the highest level Acme exposes a number of agents which can be used simply as follows:
import acme # Create an environment and an actor. environment = ... actor = ... # Run the environment loop. loop = acme.EnvironmentLoop(environment, actor) loop.run()
Acme also tries to maintain this level of simplicity while either diving deeper into the agent algorithms or by using them in more complicated settings. An overview of Acme along with more detailed descriptions of its underlying components can be found by referring to the documentation.
For a quick start, take a look at the more detailed working code examples found in the examples subdirectory, which also includes a tutorial notebook to get you started. And finally, for more information on the various agent implementations available take a look at the agents subdirectory along with the README.md associated with each agent.