{"id":16403896,"url":"https://github.com/marcometer/action-space-compositions-in-deep-reinforcement-learning","last_synced_at":"2026-06-14T18:32:30.407Z","repository":{"id":125213912,"uuid":"156387593","full_name":"MarcoMeter/Action-Space-Compositions-in-Deep-Reinforcement-Learning","owner":"MarcoMeter","description":"Application of concurrent discrete and continuous actions on two novel DRL environments to mimic human input devices.","archived":false,"fork":false,"pushed_at":"2020-01-16T12:02:25.000Z","size":120699,"stargazers_count":3,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-02-23T17:44:18.451Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":"Jupyter 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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Action Spaces in Deep Reinforcement Learning to Mimic Human Input Devices\n\nThis repository contributes the environments Shooting Birds and Beastly Rivals Onslaught, which were examined in the underlying paper (headline name).\nHyperparameters can be retrieved from the [config](https://github.com/MarcoMeter/Action-Space-Compositions-in-Deep-Reinforcement-Learning/blob/master/config/trainer_config.yaml).\nA video showing the results can be found on [Youtube](https://www.youtube.com/watch?v=Pb14i3srRWc\u0026feature=youtu.be).\n\n# Dependencies\n\nThis project was created with [Unity's ML-Agents Toolkit](https://github.com/Unity-Technologies/ml-agents) v0.7 along with the Unity Engine version 2018.2.20f.\nTo install the required python packages run:\n```\npip install -e /ml-agents/.\npip install tensorflow==1.7.*\n```\n\nCheckout the \"update\" branch if you are looking for project files using more recent dependencies (ml-agents \u0026 Unity).\n\n# Citing this paper\n\nLink to the paper:\nhttps://ieeexplore.ieee.org/document/8848080\n\n```\n@inproceedings{Pleines2019,\n  author    = {Marco Pleines and\n               Frank Zimmer and\n               Vincent{-}Pierre Berges},\n  title     = {Action Spaces in Deep Reinforcement Learning to Mimic Human Input\n               Devices},\n  booktitle = {{IEEE} Conference on Games, CoG 2019, London, United Kingdom, August\n               20-23, 2019},\n  pages     = {1--8},\n  year      = {2019},\n  url       = {https://doi.org/10.1109/CIG.2019.8848080},\n  doi       = {10.1109/CIG.2019.8848080}\n}\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmarcometer%2Faction-space-compositions-in-deep-reinforcement-learning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmarcometer%2Faction-space-compositions-in-deep-reinforcement-learning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmarcometer%2Faction-space-compositions-in-deep-reinforcement-learning/lists"}