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I recommend training in node.js and then loading and using the model in the frontend.\n\n### Example\n\nFirst install the npm-package:\n\n```bash\nnpm install quickrl.core\n```\n\nFirst create \"training.ts\" or how ever else you choose to call this file:\n\n```ts\nimport { SingleAgentEnvironment, Agents, QuickRLJS } from 'quickrl.core';\n// if you want to improve training performance, make shure you import this dependency\nrequire('@tensorflow/tfjs-node-gpu');\n\n// parameters\nconst randomSeed = 12;\nconst numTrainingEpisodes = 10000;\nconst logEvery = 500;\nconst maxIterationsPerGame = 25;\n\n// load the environment\nconst env: SingleAgentEnvironment = QuickRLJS.loadEnv('Taxi', { randomSeed: randomSeed })!;\n\n// create an agent\nconst agent: Agents.DQNAgent = new Agents.DQNAgent(env, {\n    learningRate: 0.0001,\n    discountFactor: 0.99,\n    nnLayer: [128, 128, 64],\n    epsilonStart: 1,\n    epsilonEnd: 0.01,\n    epsilonDecaySteps: 10000,\n    hiddenLayerActivation: 'relu',\n    batchSize: 32,\n    replayMemorySize: 10000,\n    replayMemoryInitSize: 1000,\n    activateDoubleDQN: true,\n    updateTargetEvery: 10000,\n});\n\n// set and initialize the agent for the environment\nenv.agent = agent;\nenv.initAgent();\n\n// train the agent\nawait env.train(numTrainingEpisodes, logEvery, maxIterationsPerGame);\n```\n\n### Saving Models\n\nAfter you trained the model, these models can be saved. Depending on whether you train on the web or in a node.js environment you have to install the specific package. The following example will show how it is done in a node environment.\n\nFirst install node `quickrl.node` package:\n\n```bash\nnpm install quickrl.node\n```\n\nNow import the Filestrategies into your training script:\n\n```ts\n...\nimport { FileStrategies } from 'quickrl.node';\n...\n```\n\nAfter the training was finished. The agent can be saved into a dedicated folder. Also make shure to save your settings so you don't lose them.\n\n```ts\n// train the agent\nawait env.train(numTrainingEpisodes, logEvery, maxIterationsPerGame);\n\nawait agent.save(\n    new FileStrategies.NodeTFModelSaver({\n        folderPath: './models/DQN/TaxiModel/',\n    })\n);\nawait agent.saveConfig(\n    new FileStrategies.NodeJSONFileSaver({\n        filePath: './models/DQN/TaxiModel/config.json',\n    })\n);\n```\n\n### Loading models\n\nPreviously saved models can also be loaded later. This way you can train the model, save it and load it at a later point in time to continue training, or you load trained models and can run benchmarks on them to analyse the results.\n\nThis is how you can load the model into a new agent for example:\n\n```ts\nconst benchmarkAgent = new Agents.DQNAgent(env);\n// load configuration\nbenchmarkAgent.loadConfig(\n    new FileStrategies.NodeJSONFileLoader({\n        filePath: './models/DQN/TaxiModel/config.json',\n    })\n);\n// load the model\nbenchmarkAgent.load(\n    new FileStrategies.NodeTFModelLoader({\n        folderPath: './models/DQN/TaxiModel/',\n    })\n);\n```\n\n## Links\n\n- [To the API Documentation](https://philippoesch.github.io/QuickRLGym.js/docs/)","readmeFilename":"README.md","homepage":"https://github.com/PhilippOesch/QuickRLGym.js#readme","repository":{"type":"git","url":"git+https://github.com/PhilippOesch/QuickRLGym.js.git"},"bugs":{"url":"https://github.com/PhilippOesch/QuickRLGym.js/issues"}}