68db549217
- All agent training demo notebooks now reference UC2. - Terminal-Processing Notebook now includes a few extra markdown cells for extra context. Additionally yaml snippets have been updated to reflect 4.0.0 schema - Request-and-Response notebook now includes a few more markdown cells for extra context as well as updated software names - General notebook cell clean up and tidying.
114 lines
2.6 KiB
Plaintext
114 lines
2.6 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Train a Single agent system using RLLib\n",
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"\n",
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"© Crown-owned copyright 2025, Defence Science and Technology Laboratory UK\n",
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"\n",
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"This notebook demonstrates how to use the ``PrimaiteRayEnv`` to train a basic PPO agent on the [UC2 scenario](./Data-Manipulation-E2E-Demonstration.ipynb)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"!primaite setup"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import yaml\n",
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"import ray\n",
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"from primaite.config.load import data_manipulation_config_path\n",
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"from primaite.session.ray_envs import PrimaiteRayEnv\n",
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"from ray.rllib.algorithms.ppo import PPOConfig\n",
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"\n",
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"# If you get an error saying this config file doesn't exist, you may need to run `primaite setup` in your command line\n",
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"# to copy the files to your user data path.\n",
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"with open(data_manipulation_config_path(), 'r') as f:\n",
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" cfg = yaml.safe_load(f)\n",
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"\n",
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"ray.init(local_mode=True)\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Create a Ray algorithm and pass it our config."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"for agent in cfg['agents']:\n",
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" if agent[\"ref\"] == \"defender\":\n",
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" agent['agent_settings']['flatten_obs'] = True\n",
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"env_config = cfg\n",
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"\n",
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"config = (\n",
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" PPOConfig()\n",
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" .environment(env=PrimaiteRayEnv, env_config=env_config)\n",
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" .env_runners(num_env_runners=0)\n",
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" .training(train_batch_size=128)\n",
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" .evaluation(evaluation_duration=1)\n",
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")\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Start the training"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"algo = config.build()\n",
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"results = algo.train()\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Evaluate the results"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"eval = algo.evaluate()"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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