# Distributed Training with Amazon SageMaker AI RL


Amazon SageMaker AI RL supports multi-core and multi-instance distributed training. Depending on your use case, training and/or environment rollout can be distributed. For example, SageMaker AI RL works for the following distributed scenarios:
+ Single training instance and multiple rollout instances of the same instance type. For an example, see the Neural Network Compression example in the [SageMaker AI examples repository](https://github.com/awslabs/amazon-sagemaker-examples/tree/master/reinforcement_learning).
+ Single trainer instance and multiple rollout instances, where different instance types for training and rollouts. For an example, see the AWS DeepRacer / AWS RoboMaker example in the [SageMaker AI examples repository](https://github.com/awslabs/amazon-sagemaker-examples/tree/master/reinforcement_learning).
+ Single trainer instance that uses multiple cores for rollout. For an example, see the Roboschool example in the [SageMaker AI examples repository](https://github.com/awslabs/amazon-sagemaker-examples/tree/master/reinforcement_learning). This is useful if the simulation environment is light-weight and can run on a single thread. 
+ Multiple instances for training and rollouts. For an example, see the Roboschool example in the [SageMaker AI examples repository](https://github.com/awslabs/amazon-sagemaker-examples/tree/master/reinforcement_learning).