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If head_compute_target and type(head_compute_target) is AmlCompute: If head_compute_name in ws.compute_targets: For using CPU VM, set SKU to STANDARD_D2_V2

It has 6 virtual CPUs to distribute work across. The head compute uses a single STANDARD_NC6 virtual machine (VM). The head node also collects data points from the worker nodes to train the neural network. The head node trains the neural network that the agent uses to make decisions. You can use a GPU-equipped head cluster to improve deep learning performance. Set the number of nodes, and the size of each node, based on your needs. These settings let you scale your compute resources up and down depending on your workload. This example uses separate compute targets for the Ray head and workers nodes. Here, you specify the name of the virtual network in your resource group. For more information on setting up your virtual network, see the workspace setup notebook in the prerequisites section. The virtual network can be in any resource group, but it should be in the same region as your workspace. experiment_name='rllib-pong-multi-node'Įxp = Experiment(workspace=ws, name=experiment_name)įor RL jobs that use multiple compute targets, you must specify a virtual network with open ports that allow worker nodes and head nodes to communicate with each other. In Azure Machine Learning, experiments are logical collections of related trials to organize job logs, history, outputs, and more. ws = om_config()Ĭreate a reinforcement learning experimentĬreate an experiment to track your reinforcement learning job.

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If you are executing this code in an Azure Machine Learning Compute Instance, the configuration file has already been created for you. Initialize a workspace object from the config.json file created in the prerequisites section. # Azure ML Reinforcement Learning importsįrom import ReinforcementLearningEstimator, Rayįrom import WorkerConfiguration # Azure ML Core importsįrom import AmlComputeįrom import ComputeTargetįrom import EnvironmentDefinitionįrom azureml.tensorboard import Tensorboard Import the necessary Python packages to run the rest of this example.

Specifying a configured virtual network.

However, managing a distributed RL environment can be a complex undertaking.Īzure Machine Learning provides the framework to manage these complexities to scale out your RL workloads. One way to improve performance of RL jobs is by parallelizing work so that multiple training agents can act and learn simultaneously. The process of iterating through simulation and retraining a DNN is computationally expensive, and requires a lot of data. This means that the agent has beaten its opponent by an average of at least 18 points in matches up to 21. Training ends when the agent reaches an average reward score of 18 in a training epoch. It's common to use deep neural networks (DNN) to perform this optimization in RL. Over many iterations, the training agent learns to choose the action, based on its current state, that optimizes for the sum of expected future rewards. In this example, the agent gets a positive reward when it scores a point and a negative reward when a point is scored against it. RL uses rewards to tell the agent if its decisions are successful. It looks at the state of the game (an RGB image of the screen) to make a decision. Training agents make a decision every frame of the game to move the paddle up, down, or stay in place. Your training agents learn to play Pong in a simulated environment. While other machine learning techniques learn by passively taking input data and finding patterns within it, RL uses training agents to actively make decisions and learn from their outcomes.

Reinforcement learning (RL) is an approach to machine learning that learns by doing.

Run the virtual network to open network ports used for distributed reinforcement learning.

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Install the Azure Machine Learning RL SDK: pip install -upgrade azureml-contrib-reinforcementlearning.

Install the Azure Machine Learning SDK.

Run the sample notebook /how-to-use-azureml/reinforcement-learning/atari-on-distributed-compute/pong_rllib.ipynb.

Run the virtual network setup notebook located at /how-to-use-azureml/reinforcement-learning/setup/devenv_setup.ipynb to open network ports used for distributed reinforcement learning.

Clone the how-to-use-azureml folder instead of tutorials.

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Learn how to clone sample notebooks in Tutorial: Train and deploy a model.You can quickly clone and run the reinforcement sample notebooks on an Azure Machine Learning compute instance. We recommend you try Azure Machine Learning compute instance for the fastest start-up experience. Run this code in either of these environments. This article is based on the RLlib Pong example that can be found in the Azure Machine Learning notebook GitHub repository.

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