Distributed Training with Amazon SageMaker RL

Amazon SageMaker RL supports multi-core and multi-instance distributed training. Depending on your use case, training and/or environment rollout can be distributed. For example, Amazon SageMaker 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 Amazon SageMaker examples repository at 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 Amazon SageMaker examples repository at 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 Amazon SageMaker examples repository at 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 Amazon SageMaker examples repository at https://github.com/awslabs/amazon-sagemaker-examples/tree/master/reinforcement_learning.