Policy Gradient Methods

MyGym: Modular Toolkit for Visuomotor Robotic Tasks

We introduce myGym, a toolkit suitable for fast prototyping of neural networks in the area of robotic manipulation and navigation. Our toolbox is fully modular, enabling users to train their algorithms on different robots, environments, and tasks. We also include pretrained neural network modules for the real-time vision that allows training visuomotor tasks with sim2real transfer. The visual modules can be easily retrained using the dataset generation pipeline with domain augmentation and randomization. Moreover, myGym provides automatic evaluation methods and baselines that help the user to directly compare their trained model with the state-of-the-art algorithms. We additionally present a novel metric, called learnability, to compare the general learning capability of algorithms in different settings, where the complexity of the environment, robot, and the task is systematically manipulated. The learnability score tracks differences between the performance of algorithms in increasingly challenging setup conditions, and thus allows the user to compare different models in a more systematic fashion. The code is accessible at https://github.com/incognite-lab/myGym

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Imitation Learning 1 50.00%
OpenAI Gym 1 50.00%

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