{"id":13935860,"url":"https://github.com/cpnota/all-example-project","last_synced_at":"2025-10-08T13:20:48.792Z","repository":{"id":131345247,"uuid":"233137804","full_name":"cpnota/all-example-project","owner":"cpnota","description":"An example project demonstrating how the Autonomous Learning Library can be used to build new reinforcement learning 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About\n\nThis repository shows how  [Autonomous Learning Library](https://github.com/cpnota/autonomous-learning-library) can be used to build new reinforcement learning agents.\nIn particular, it contains a *model based* agent that predicts future frames and uses them to guide decision making.\n\n## Instructions\n\nFirst, you'll need the latest version of [Pytorch](https://pytorch.org).\nIf you wish to view [Tensorboard](https://github.com/tensorflow/tensorboard) logs, you'll also need to grab a copy of that (it also comes with `tensorflow`).\nThen, you'll need to install the `autonomous-learning-library` along with the Atari environments:\n\n\n```\npip install autonomous-learning-library[atari]\n```\n\nUnfortunately, the current IP holders for the Atari library made it more difficult to acquire a license and use the ROMs than it used to be.\nIf you have a license to use the ROMs, you can try [AutoROM](https://github.com/PettingZoo-Team/AutoROM).\n\n## Usage\n\nYou can run the agent as well as a baseline DQN agent using:\n\n```\npython main.py Pong\n```\n\nYou can track progress using:\n\n```\ntensorboard --logdir runs\n```\n\nOnce the script has finished (could take a long time, especially if you do not have a fast GPU!), you can see the final results using:\n\n```\npython plot.py\n```\n\n## Results\n\nFor us, the above instructions produced the following results:\n\n![results](results.png)\n\nAs you can see, this agent isn't very good!\nOn the other hand, the purpose of this agent was not performance,\nbut to demonstrate the utility of the `autonomous-learning-library` in developing *new* agents not included in the original library.\nMaybe you can come up with ways of improving this agent!\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcpnota%2Fall-example-project","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcpnota%2Fall-example-project","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcpnota%2Fall-example-project/lists"}