Achieving reproducible results in reinforcement learning experiments can be notoriously difficult; with the added hardware artifacts of physical robots, this becomes an even more challenging feat. In this guest blog post, Oliver presents an overview of his experience as an early tester working with SenseAct to reproduce the UR-Reacher-2 experiment.
We introduce six reinforcement learning benchmark tasks based on three commercially available robots. These tasks are developed in SenseAct, a new open-source framework for implementing real-time reinforcement learning tasks. We furthermore provide benchmarking results from our evaluation of several state of the art learning algorithms for continuous control on these tasks.