Training a reinforcement learning Agent with Unity and Amazon SageMaker RL

 Training a reinforcement learning Agent with Unity and Amazon SageMaker RL

Unity is one of the most popular game engines that has been adopted not only for video game development but also by industries such as film and automotive. Unity offers tools to create virtual simulated environments with customizable physics, landscapes, and characters. The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables developers to train reinforcement learning (RL) agents against the environments created on Unity.

Reinforcement learning is an area of machine learning (ML) that teaches a software agent how to take actions in an environment in order to maximize a long-term objective. For more information, see Amazon SageMaker RL – Managed Reinforcement Learning with Amazon SageMaker. ML-Agents is becoming an increasingly popular tool among many gaming companies for use cases such as game level difficulty design, bug fixing, and cheat detection. Currently, ML-Agents is used to train agents locally, and can’t scale to efficiently use more computing resources. You have to train RL agents on a local Unity engine for an extensive amount of time before obtaining the trained model. The process is time-consuming and not scalable for processing large amounts of data.

In this post, we demonstrate a solution by integrating the ML-Agents Unity interface with Amazon SageMaker RL, allowing you to train RL agents on Amazon SageMaker in a fully managed and scalable fashion.

Overview of solution

SageMaker is a fully managed service that enables fast model development. It provides many built-in features to assist you with training, tuning, debugging, and model deployment. SageMaker RL builds on top of SageMaker, adding pre-built RL libraries and making it easy to integrate with different simulation environments. You can use built-in deep learning frameworks such as TensorFlow and PyTorch with various built-in RL algorithms from the RLlib library to train RL policies. Infrastructures for training and inference are fully managed by SageMaker, so you can focus on RL formulation. SageMaker RL also provides a set of Jupyter notebooks, demonstrating varieties of domain RL applications in robotics, operations research, finance, and more.

The following diagram illustrates our solution architecture.

In this post, we walk through the specifics of training an RL agent on SageMaker by interacting with the sample Unity environment. To access the complete notebook for this post, see the SageMaker notebook example on GitHub.

Setting up your environments

To get started, we import the needed Python libraries and set up environments for permissions and configurations. The following code contains the steps to set up an Amazon Simple Storage Service (Amazon S3) bucket, define the training job prefix, specify the training job location,


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