Unity3D Game with Amazon SageMaker RL


This notebook has been tested in a TensorFlow 1.15 Python 3.7 CPU Optimized kernel.

Introduction

Unity is currently the most popular gaming engine used by game developers around the world. Unity engine can be used to create 3D, 2D, virtual reality, and augmented reality games, as well as simulations and other experiences. ML-Agents is an open-sourced toolkit developed by Unity to enable games and simulations to serve as environments for training intelligent agents. It provides capabilities on how to interact with Unity executables as well as how to train a RL agent. For how to use ML-Agents to train a Unity game agent on SageMaker, please refer to this notebook.

In this notebook, we are using capabilities from ML-Agent to interact with Unity executables, but leveraging Amazon SageMaker RL to train the game agent. We will show how you can apply reinforcement learning to train an agent to play against a Unity game with Amazon SageMaker RL.

Pre-requisites

Imports

To get started, we’ll import the Python libraries we need, set up the environment with a few prerequisites for permissions and configurations.

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import sagemaker
import boto3
import sys
import os
import glob
import re
import subprocess
import numpy as np
from IPython.display import HTML
import time
from time import gmtime, strftime

sys.path.append("common")
from misc import get_execution_role, wait_for_s3_object
from docker_utils import build_and_push_docker_image
from sagemaker.rl import RLEstimator, RLToolkit, RLFramework

Setup S3 bucket

Set up the linkage and authentication to the S3 bucket that you want to use for checkpoint and the metadata.

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sage_session = sagemaker.session.Session()
s3_bucket = sage_session.default_bucket()
s3_output_path = "s3://{}/".format(s3_bucket)
print("S3 bucket path: {}".format(s3_output_path))

Define Variables

We define variables such as the job prefix for the training jobs and the image path for the container (only when this is BYOC).

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# create a descriptive job name
job_name_prefix = "rl-unity-ray"

Configure where training happens

You can train your RL training jobs using the SageMaker notebook instance or local notebook instance. In both of these scenarios, you can run the following in either local or SageMaker modes. The local mode uses the SageMaker Python SDK to run your code in a local container before deploying to SageMaker. This can speed up iterative testing and debugging while using the same familiar Python SDK interface. You just need to set local_mode = True.

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# run in local_mode on this machine, or as a SageMaker TrainingJob?
local_mode = False

if local_mode:
    instance_type = "local"
else:
    # If on SageMaker, pick the instance type
    instance_type = "ml.c5.2xlarge"

if "ml.p" in instance_type or "ml.g" in instance_type:
    cpu_or_gpu = "gpu"
else:
    cpu_or_gpu = "cpu"

Create an IAM role

Either get the execution role when running from a SageMaker notebook instance role = sagemaker.get_execution_role() or, when running from local notebook instance, use utils method role = get_execution_role() to create an execution role.

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try:
    role = sagemaker.get_execution_role()
except:
    role = get_execution_role()

print("Using IAM role arn: {}".format(role))

Install docker for local mode

In order to work in local mode, you need to have docker installed. When running from you local machine, please make sure that you have docker and docker-compose (for local CPU machines) and nvidia-docker (for local GPU machines) installed. Alternatively, when running from a SageMaker notebook instance, you can simply run the following script to install dependenceis.

Note, you can only run a single local notebook at one time.

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# only run from SageMaker notebook instance
if local_mode:
    !/bin/bash ./common/setup.sh

Use Unity Example Environment

The Unity ML-Agents Toolkit provides an expanding set of example environments. You can specify one of the environments name in the config and SageMaker RL will start to train a RL agent against that environment. The training code loads an example environment (Basic by default) from the default registry and start the training. Currently SageMaker RL can only support example environment with a single agent.

Write the Training Code

The training code is written in the file “train-unity.py” which is uploaded in the /src directory. First import the environment files and the preset files, and then define the main() function.

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!pygmentize src/train-unity.py

Train the RL model using the Python SDK Script mode

If you are using local mode, the training will run on the notebook instance. When using SageMaker for training, you can select a GPU or CPU instance. The RLEstimator is used for training RL jobs.

  1. Specify the source directory where the environment, presets and training code is uploaded.

  2. Specify the entry point as the training code

  3. Specify the choice of RL toolkit and framework. This automatically resolves to the ECR path for the RL Container.

  4. Define the training parameters such as the instance count, job name, S3 path for output and job name.

  5. Specify the hyperparameters for the RL agent algorithm.

  6. Define the metrics definitions that you are interested in capturing in your logs. These can also be visualized in CloudWatch and SageMaker Notebooks.

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%%time

ray_version = "0.8.5"
suffix = "py36"

image_name = "462105765813.dkr.ecr.{region}.amazonaws.com/sagemaker-rl-ray-container:ray-{ray_version}-tf-{cpu_or_gpu}-{suffix}".format(
    region=sage_session.boto_region_name,
    ray_version=ray_version,
    cpu_or_gpu=cpu_or_gpu,
    suffix=suffix,
)
metric_definitions = RLEstimator.default_metric_definitions(RLToolkit.RAY)

estimator = RLEstimator(
    entry_point="train-unity.py",
    source_dir="src",
    dependencies=["common/sagemaker_rl"],
    image_uri=image_name,
    role=role,
    instance_type=instance_type,
    instance_count=1,
    output_path=s3_output_path,
    base_job_name=job_name_prefix,
    metric_definitions=metric_definitions,
    debugger_hook_config=False,
    environment={"PYTHONUNBUFFERED": "1"},
    hyperparameters={
        # Attention scientists!  You can override any Ray algorithm parameter here:
        # "rl.training.config.env_config.env_name": "Basic",
        # "rl.training.stop.timesteps_total": 10000,
        # "rl.training.config.num_sgd_iter": 10,
    },
)

estimator.fit(wait=True)
job_name = estimator.latest_training_job.job_name
print("Training job: %s" % job_name)

Plot metrics for training job

We can see the reward metric of the training as it’s running, using algorithm metrics that are recorded in CloudWatch metrics. We can plot this to see the performance of the model over time.

TrainingJobAnalytics could not parse the CloudWatch log immediately after running the training procedure, please wait for the training is done and stablized before running the following cell again!

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%matplotlib inline
from sagemaker.analytics import TrainingJobAnalytics
import time

sm_client = boto3.client(service_name="sagemaker")

state = sm_client.describe_training_job(TrainingJobName=job_name).get("SecondaryStatus")

if not local_mode:
    while state == "Starting":
        state = sm_client.describe_training_job(TrainingJobName=job_name).get("SecondaryStatus")
        print("Training job starting...")
        time.sleep(30)
    print("Training job started. Waiting for algorithm metric...")
    df = TrainingJobAnalytics(job_name, ["episode_reward_mean"]).dataframe()
    num_metrics = len(df)
    if num_metrics == 0:
        print("No algorithm metrics found in CloudWatch, please check later.")
    else:
        plt = df.plot(x="timestamp", y="value", figsize=(12, 5), legend=True, style="b-")
        plt.set_ylabel("Mean reward per episode")
        plt.set_xlabel("Training time (s)")
else:
    print("Can't plot metrics in local mode.")

(Optional) Bring customized Unity environment

We have shown how to load a sample environment from the Unity Environment Registry and train a RL agent. However, you can use your custom Unity executable as the environment to start the training. 1. Create a Unity executable of your Unity environment for Linux platform. Please make sure your environment only contains one single agent. 2. Upload the executable file, dependency data files and library files to s3. 3. Specify the s3 path as a train channel of the training job.

Train the RL model using the Python SDK Script mode with provided Unity executables

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# %%time

# metric_definitions = RLEstimator.default_metric_definitions(RLToolkit.RAY)

# estimator = RLEstimator(entry_point="train-unity.py",
#                         source_dir='src',
#                         dependencies=["common/sagemaker_rl"],
#                         image_uri=custom_image_name,
#                         role=role,
#                         instance_type=instance_type,
#                         instance_count=1,
#                         output_path=s3_output_path,
#                         base_job_name=job_name_prefix,
#                         metric_definitions=metric_definitions,
#                         hyperparameters={
#                           # Attention scientists!  You can override any Ray algorithm parameter here:
#                           # "rl.training.config.env_config.env_name": "<file name of your Unity executables> e.g. basic_env_linux.x86_64",
#                           # "rl.training.stop.timesteps_total": 1000,
#                           # "rl.training.config.num_sgd_iter": 10,
#                         }
#                     )
# s3_binary_path = '<s3 path for your Unity files> e.g. s3://bucket/unity-data'
# estimator.fit({'train': s3_binary_path}, wait=local_mode)
# job_name = estimator.latest_training_job.job_name
# print("Training job: %s" % job_name)

Evaluation of RL models

We use the last checkpointed model to run evaluation for the RL Agent.

Load checkpointed model

Checkpointed data from the previously trained models will be passed on for evaluation / inference in the checkpoint channel. In local mode, we can simply use the local directory, whereas in the SageMaker mode, it needs to be moved to S3 first.

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tmp_dir = "/tmp/{}".format(job_name)
os.system("mkdir {}".format(tmp_dir))
print("Create local folder {}".format(tmp_dir))

if local_mode:
    model_tar_key = "{}/model.tar.gz".format(job_name)
else:
    model_tar_key = "{}/output/model.tar.gz".format(job_name)

local_checkpoint_dir = "{}/model".format(tmp_dir)

wait_for_s3_object(s3_bucket, model_tar_key, tmp_dir, training_job_name=job_name)

if not os.path.isfile("{}/model.tar.gz".format(tmp_dir)):
    raise FileNotFoundError("File model.tar.gz not found")

os.system("mkdir -p {}".format(local_checkpoint_dir))
os.system("tar -xvzf {}/model.tar.gz -C {}".format(tmp_dir, local_checkpoint_dir))

print("Checkpoint directory {}".format(local_checkpoint_dir))
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if local_mode:
    checkpoint_path = "file://{}".format(local_checkpoint_dir)
    print("Local checkpoint file path: {}".format(local_checkpoint_dir))
else:
    checkpoint_path = "s3://{}/{}/checkpoint/".format(s3_bucket, job_name)
    if not os.listdir(local_checkpoint_dir):
        raise FileNotFoundError("Checkpoint files not found under the path")
    os.system("aws s3 cp --recursive {} {}".format(local_checkpoint_dir, checkpoint_path))
    print("S3 checkpoint file path: {}".format(checkpoint_path))
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%%time

estimator_eval = RLEstimator(
    entry_point="evaluate-unity.py",
    source_dir="src",
    dependencies=["common/sagemaker_rl"],
    image_uri=image_name,
    role=role,
    instance_type=instance_type,
    instance_count=1,
    base_job_name=job_name_prefix + "-evaluation",
    hyperparameters={
        "evaluate_episodes": 5,
        "algorithm": "PPO",
        # change the env name here acoording to the traine environment
        "env": "Basic",
    },
)

estimator_eval.fit(
    {"model": checkpoint_path}, wait=local_mode
)  # specify a train channel for the custom Unity executable option

job_name = estimator_eval.latest_training_job.job_name

Model deployment

Now let us deploy the RL policy so that we can get the optimal action, given an environment observation.

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from sagemaker.tensorflow.model import TensorFlowModel

model = TensorFlowModel(model_data=estimator.model_data, framework_version="2.5.1", role=role)

predictor = model.deploy(initial_instance_count=1, instance_type=instance_type)
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# ray 1.6.0 requires all the following inputs, ray 0.8.5 or below remove 'timestep'
# 'prev_action', 'is_training', 'prev_reward', 'seq_lens' and 'timestep' are placeholders for this example
# they won't affect prediction results

# observation shape passed here must match with the environment specs
input = {
    "inputs": {
        "observations": np.ones(shape=(1, 20)).tolist(),
        "prev_action": [0, 0],
        "is_training": False,
        "prev_reward": -1,
        "seq_lens": -1,
        "timestep": 1,
    }
}
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result = predictor.predict(input)

result["outputs"]

Clean up endpoint

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predictor.delete_model()
predictor.delete_endpoint()
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