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        "description": "<p align=\"center\"><img src=\"http://cherry-rl.net/assets/img/cherry_full.png\" height=\"150px\" /></p>\n\n--------------------------------------------------------------------------------\n\n[![Build Status](https://travis-ci.org/learnables/cherry.svg?branch=master)](https://travis-ci.org/learnables/cherry)\n\nCherry is a reinforcement learning framework for researchers built on top of PyTorch.\n\nUnlike other reinforcement learning implementations, cherry doesn't implement a single monolithic  interface to existing algorithms.\nInstead, it provides you with low-level, common tools to write your own algorithms.\nDrawing from the UNIX philosophy, each tool strives to be as independent from the rest of the framework as possible.\nSo if you don't like a specific tool, you don\u2019t need to use it.\n\n**Features**\n\n* Pythonic and low-level interface *\u00e0 la* Pytorch.\n* Support for tabular (!) and function approximation algorithms.\n* Various OpenAI Gym environment wrappers.\n* Helper functions for popular algorithms. (e.g. A2C, DDPG, TRPO, PPO, SAC)\n* Logging, visualization, and debugging tools.\n* Painless and efficient distributed training on CPUs and GPUs.\n* Unit, integration, and regression tested, continuously integrated.\n\nTo learn more about the tools and philosophy behind cherry, check out our [Getting Started tutorial](http://cherry-rl.net/tutorials/getting_started/).\n\n## Example\n\nThe following snippet showcases some of the tools offered by cherry.\n\n~~~python\nimport cherry as ch\n\n# Wrap environments\nenv = gym.make('CartPole-v0')\nenv = ch.envs.Logger(env, interval=1000)\nenv = ch.envs.Torch(env)\n\npolicy = PolicyNet()\noptimizer = optim.Adam(policy.parameters(), lr=1e-2)\nreplay = ch.ExperienceReplay()  # Manage transitions\n\nfor step in range(1000):\n    state = env.reset()\n    while True:\n        mass = Categorical(policy(state))\n        action = mass.sample()\n        log_prob = mass.log_prob(action)\n        next_state, reward, done, _ = env.step(action)\n\n        # Build the ExperienceReplay\n        replay.append(state, action, reward, next_state, done, log_prob=log_prob)\n        if done:\n            break\n        else:\n            state = next_state\n\n    # Discounting and normalizing rewards\n    rewards = ch.td.discount(0.99, replay.reward(), replay.done())\n    rewards = ch.normalize(rewards)\n\n    loss = -th.sum(replay.log_prob() * rewards)\n    optimizer.zero_grad()\n    loss.backward()\n    optimizer.step()\n    replay.empty()\n~~~\n\nMany more high-quality examples are available in the [examples/](./examples/) folder.\n\n## Installation\n\n**Note** Cherry is considered in early alpha release. Stuff might break.\n\n```\npip install cherry-rl\n```\n\n## Documentation\n\nDocumentation and tutorials are available on cherry\u2019s website: [http://cherry-rl.net](http://cherry-rl.net).\n\n## Contributing\n\nFirst, thanks for your consideration in contributing to cherry.\nHere are a couple of guidelines we strive to follow.\n\n* It's always a good idea to open an issue first, where we can discuss how to best proceed.\n* If you want to contribute a new example using cherry, it would preferably stand in a single file.\n* If you would like to contribute a new feature to the core library, we suggest to first implement an example showcasing your new functionality. Doing so is quite useful:\n    * it allows for automatic testing,\n    * it ensures that the functionality is correctly implemented,\n    * it shows users how to use your functionality, and\n    * it gives a concrete example when discussing the best way to merge your implementation.\n\nWe don't have forums, but are happy to discuss with you on slack.\nMake sure to send an email to [smr.arnold@gmail.com](mailto:smr.arnold@gmail.com) to get an invite.\n\n## Acknowledgements\n\nCherry draws inspiration from many reinforcement learning implementations, including\n\n* [OpenAI Baselines](https://github.com/openai/baselines),\n* John Schulman's [implementations](https://github.com/joschu/modular_rl)\n* Ilya Kostrikov's [implementations](https://github.com/ikostrikov/pytorch-a2c-ppo-acktr),\n* Shangtong Zhang's [implementations](https://github.com/ShangtongZhang/DeepRL),\n* Dave Abel's [implementations](https://github.com/david-abel/simple_rl/),\n* Vitchyr Pong's [implementations](https://github.com/vitchyr/rlkit),\n* Kai Arulkumaran's [implementations](https://github.com/Kaixhin/spinning-up-basic),\n* [RLLab](https://github.com/rll/rllab) / [Garage](https://github.com/rlworkgroup/garage).\n\n\n## Why 'cherry' ?\n\nBecause it's the sweetest part of [the cake](https://twitter.com/ylecun/status/1097532314614034433).",
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