Simion Zoo: A Workbench for Distributed Experimentation with Reinforcement Learning for Continuous Control Tasks
This provides a practical solution for researchers and practitioners in reinforcement learning, though it is incremental as it builds on existing software packages with added features.
The authors tackled the challenge of conducting distributed reinforcement learning experiments for continuous control tasks by developing Simion Zoo, a workbench with a GUI, GPU support, and concurrent metaparameter exploration, resulting in a tool that facilitates design, execution, and analysis of RL applications.
We present Simion Zoo, a Reinforcement Learning (RL) workbench that provides a complete set of tools to design, run, and analyze the results,both statistically and visually, of RL control applications. The main features that set apart Simion Zoo from similar software packages are its easy-to-use GUI, its support for distributed execution including deployment over graphics processing units (GPUs) , and the possibility to explore concurrently the RL metaparameter space, which is key to successful RL experimentation.