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            "Topic :: Scientific/Engineering",
            "Topic :: Scientific/Engineering :: Artificial Intelligence",
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        "description": "# RecSim: A Configurable Recommender Systems Simulation Platform\n\nRecSim is a configurable platform for authoring simulation environments for\nrecommender systems (RSs) that naturally supports **sequential interaction**\nwith users. RecSim allows the creation of new environments that reflect\nparticular aspects of user behavior and item structure at a level of abstraction\nwell-suited to pushing the limits of current reinforcement learning (RL) and RS\ntechniques in sequential interactive recommendation problems. Environments can\nbe easily configured that vary assumptions about: user preferences and item\nfamiliarity; user latent state and its dynamics; and choice models and other\nuser response behavior. We outline how RecSim offers value to RL and RS\nresearchers and practitioners, and how it can serve as a vehicle for\nacademic-industrial collaboration. For a detailed description of the RecSim\narchitecture please read [Ie et al](https://arxiv.org/abs/1909.04847). Please\ncite the paper if you use the code from this repository in your work.\n\n### Bibtex\n\n```\n@article{ie2019recsim,\n    title={RecSim: A Configurable Simulation Platform for Recommender Systems},\n    author={Eugene Ie and Chih-wei Hsu and Martin Mladenov and Vihan Jain and Sanmit Narvekar and Jing Wang and Rui Wu and Craig Boutilier},\n    year={2019},\n    eprint={1909.04847},\n    archivePrefix={arXiv},\n    primaryClass={cs.LG}\n}\n```\n\n<a id='Disclaimer'></a>\n\n## Disclaimer\n\nThis is not an officially supported Google product.\n\n## What's new\n\n*   **12/13/2019:** Added (abstract) classes for both multi-user environments\n    and agents. Added bandit algorithms for generalized linear models.\n\n## Installation and Sample Usage\n\nIt is recommended to install RecSim using (https://pypi.org/project/recsim/):\n\n```shell\npip install recsim\n```\n\nHowever, the latest version of Dopamine is not in PyPI as of December, 2019. We\nwant to install the latest version from Dopamine's repository like the following\nbefore we install RecSim. Note that Dopamine requires Tensorflow 1.15.0 which is\nthe final 1.x release including GPU support for Ubuntu and Windows.\n\n```\npip install git+https://github.com/google/dopamine.git\n```\n\nHere are some sample commands you could use for testing the installation:\n\n```\ngit clone https://github.com/google-research/recsim\ncd recsim/recsim\npython main.py --logtostderr \\\n  --base_dir=\"/tmp/recsim/interest_exploration_full_slate_q\" \\\n  --agent_name=full_slate_q \\\n  --environment_name=interest_exploration \\\n  --episode_log_file='episode_logs.tfrecord' \\\n  --gin_bindings=simulator.runner_lib.Runner.max_steps_per_episode=100 \\\n  --gin_bindings=simulator.runner_lib.TrainRunner.num_iterations=10 \\\n  --gin_bindings=simulator.runner_lib.TrainRunner.max_training_steps=100 \\\n  --gin_bindings=simulator.runner_lib.EvalRunner.max_eval_episodes=5\n```\n\nYou could then start a tensorboard and view the output\n\n```\ntensorboard --logdir=/tmp/recsim/interest_exploration_full_slate_q/ --port=2222\n```\n\nYou could also find the simulated logs in /tmp/recsim/episode_logs.tfrecord\n\n## Tutorials\n\nTo get started, please check out our Colab tutorials. In\n[**RecSim: Overview**](recsim/colab/RecSim_Overview.ipynb),\nwe give a brief overview about RecSim. We then talk about each configurable\ncomponent:\n[**environment**](recsim/colab/RecSim_Developing_an_Environment.ipynb)\nand\n[**recommender agent**](recsim/colab/RecSim_Developing_an_Agent.ipynb).\n\n## Documentation\n\n\nPlease refer to the [white paper](http://arxiv.org/abs/1909.04847) for the\nhigh-level design.\n\n\n",
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