# TensorFlow Agents A TensorFlow library for implementing, testing, and deploying contextual-bandit and reinforcement-learning algorithms. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Simulated Spotify Listening Experiences for Reinforcement Learning with TensorFlow and TF-Agents DevFeed: [Simulated Spotify Listening Experiences for Reinforcement Learning with TensorFlow and TF-Agents]() Original publisher: [Read original article]() Author: TensorFlow Blog (noreply@blogger.com) Published: 2023-10-19T19:00:00Z Content type: article Language: en Sources: [The TensorFlow Blog]() Topics: [TensorFlow Agents](), [recommendation systems](), [AI, ML & Data Engineering](), [Training AI Models](), [TFX]() Tags: [ai](), [documentation](), [keras](), [learn](), [machine-learning](), [offline](), [reinforcement-learning](), [tensorflow](), [tensorflow-agents](), [tfx]() ### AI overview Spotify describes using TensorFlow and TF-Agents to build an offline simulator for reinforcement-learning-based music recommendations. The simulator supported training and evaluating recommendation models and RL agents, with offline estimates reported as strongly correlated with live experimental results. ### Source excerpt Posted by Surya Kanoria, Joseph Cauteruccio, Federico Tomasi, Kamil Ciosek, Matteo Rinaldi, and Zhenwen Dai - Spotify Introduction Many of our music recommendation problems involve providing users with ordered sets of items that satisfy users' listening preferences and intent at that point in time. We base current recommendations on previous interactions with our application and, in the abstract, are faced with a sequential decision making process as we continually recommend content to users. Reinforcement Learning (RL) is an established tool for sequential decision making that can be leveraged to solve sequential recommendation problems. We decided to explore how RL could be used to craft listening experiences for users. Before we could start training Agents, we needed to pick a RL library that allowed us to easily prototype, test, and potentially deploy our solutions. At Spotify we leverage TensorFlow and the extended TensorFlow Ecosystem (TFX, TensorFlow Serving, and so on) as part of our production Machine Learning Stack. We made the decision early on to leverage TensorFlow Agents as our RL Library of choice, knowing that integrating our experiments with our production systems would be vastly more efficient down the line. One missing bit of technology we required was an offline Spotify environment we could use to prototype, analyze, explore, and train Agents offline prior to online testing. The flexibility of the TF-Agents library, coupled with the broader advantages of TensorFlow and its ecosystem, allowed us to cleanly design a robust and extendable offline Spotify simulator. We based our simulator design on TF-Agents Environment primitives and using this simulator we developed, trained and evaluated sequential models for item recommendations, vanilla RL Agents (PPG, DQN) and a modified deep Q-Network, which we call the Action-Head DQN (AH-DQN), that addressed the specific challenges imposed by the large state and action space of our RL formulation. Through li ## Building a board game with the TFLite plugin for Flutter DevFeed: [Building a board game with the TFLite plugin for Flutter]() Original publisher: [Read original article]() Author: TensorFlow Blog (noreply@blogger.com) Published: 2023-10-18T17:00:00Z Content type: tutorial Language: en Sources: [The TensorFlow Blog]() Topics: [Flutter](), [TensorFlow Lite](), [Inference](), [Reinforcement learning](), [Tensorflow](), [TensorFlow Agents](), [cross-platform](), [Mobile](), [board-game](), [Android](), [iOS]() Tags: [android](), [building](), [cross-platform](), [flutter](), [games](), [how-to](), [inference](), [ios](), [learn](), [mobile](), [plugin](), [reinforcement-learning](), [tensorflow](), [tensorflow-agents](), [tensorflow-lite](), [tutorial](), [tutorials]() ### AI overview This tutorial shows how to port a TensorFlow Lite reinforcement-learning board game to Flutter. It loads a converted model, runs inference to choose the next move, and renders the game for Android and iOS using the Flutter frontend and TensorFlow Lite plugin. ### Source excerpt Posted by Wei Wei, Developer Advocate In our previous blog posts Building a board game app with TensorFlow: a new TensorFlow Lite reference app and Building a reinforcement learning agent with JAX, and deploying it on Android with TensorFlow Lite, we demonstrated how to train a reinforcement learning (RL) agent with TensorFlow, TensorFlow Agents and JAX respectively, and then deploy the converted TFLite model in an Android app using TensorFlow Lite, to play a simple board game 'Plane Strike'. While these end-to-end tutorials are helpful for Android developers, we have heard from the Flutter developer community that it would be interesting to make the app cross-platform. Inspired by the officially released TensorFlow Lite Plugin for Flutter recently, we are going to write one last tutorial and port the app to Flutter. Since we already have the model trained with TensorFlow and converted to TFLite, we can just load the model with TFLite interpreter: void _loadModel() async { // Create the interpreter _interpreter = await Interpreter.fromAsset(_modelFile); } Then we pass in the user board state and help the game agent identify the most promising position to strike next (please refer to our previous blog posts if you need a refresher on the game rules) by running TFLite inference: int predict(List> boardState) { var input = [boardState]; var output = List.filled(_boardSize * _boardSize, 0) .reshape([1, _boardSize * _boardSize]); // Run inference _interpreter.run(input, output); // Argmax double max = output[0][0 ]; int maxIdx = 0; for (int i = 1; i < _boardSize * _boardSize; i++) { if (max < output[0][i]) { maxIdx = i; max = output[0][i]; } } return maxIdx; } That's it! With some additional Flutter frontend code to render the game boards and track game progress, we can immediately run the game on both Android and iOS (currently the plugin only supports these two mobile platforms). You can find the complete code on GitHub. If you want to dig digger, there ar ## Attend our first Developer Summit on Recommendation Systems DevFeed: [Attend our first Developer Summit on Recommendation Systems]() Original publisher: [Read original article]() Author: TensorFlow Blog (noreply@blogger.com) Published: 2023-05-26T14:30:00Z Content type: news Language: en Sources: [The TensorFlow Blog]() Topics: [recommendation systems](), [Language models](), [TensorFlow Agents]() Tags: [developer](), [event](), [generative-ai](), [large-language-models-llms](), [recommendation-systems](), [tensorflow-agents](), [tensorflow-ranking](), [tensorflow-recommenders]() ### AI overview Announcement of an online Developer Summit on Recommendation Systems covering TensorFlow recommendation products, LLM-augmented recommenders, and generative retrieval research. ### Source excerpt Posted by Wei Wei, Developer Advocate Register for the Summit here! Recommendation systems are everywhere. They power our favorite websites, apps, and services, helping us find the things we enjoy. But how do modern recommenders work? What are the key components and how do they fit together? How can we make them even better? Since we launched our recommendation system landing page last year, we have heard many positive feedback from our developer community. While many developers find the new consolidated page very useful to get started with our suite of products, they are also eager to learn more about how to best leverage them to build powerful in-house recommenders for their own business needs. This is why we are very excited to announce our first-ever Developer Summit on Recommendation Systems (registration is open now). This event will be held online on June 9, 2023 10AM - 12:15PM US Pacific Time and it will bring together many Google engineers who authored our suite of products to share their insights and expertise in recommendation systems. At this summit, we will not only cover specific products (such as TensorFlow Recommenders, TensorFlow Ranking, and TensorFlow Agents), share ideas on augmenting recommenders with Large Language Models (LLMs), but also discuss Google's cutting edge recommendation system research (e.g., generative retrieval using generative AI techniques). This Developer Summit is the perfect event for anyone who wants to learn more about recommendation systems. Whether you're just getting started or a seasoned practitioner in this exciting domain, you're sure to find something valuable at this event. We look forward to (virtually) meeting you there!