https://github.com/adiehl96/neurosmash
A biologically plausible deep learning reinforcement agent for Neurosmash
Science Score: 23.0%
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A biologically plausible deep learning reinforcement agent for Neurosmash
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Created over 5 years ago
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# Neurosmash A plausible deep learning reinforcement agent for the Neurosmash environment. ## Deep Q-Learning Agent Two reinforcement learning agents have been implemented. A conventional deep Q-learning agent and a biologically more plausible agent using [Hindsight Experience Replay](https://arxiv.org/abs/1707.01495). Both agents make use of the following techniques: * Convolutional Layers * Backpropagation * [Frame Stacking](https://arxiv.org/abs/1312.5602) * [LP Pooling](https://arxiv.org/abs/1204.3968) * [Huber Loss](https://doi.org/10.1007/s00521-020-04741-w) * [Frozen Target Network](https://arxiv.org/abs/1312.5602) Aside from this, the vanilla implementations used * [Memory Replay](https://doi.org/10.1109/ALLERTON.2018.8636075) The biologically plausible agent implements * [Hindsight Experience Replay](https://arxiv.org/abs/1707.01495) ## Requirements To run the program, make sure you have installed the dependencies listed in environment.yml. We recommend creating a conda environment for every project. You can do this with the following command: `conda env create --file environment.yml` ## Usage To run the networks take a look at the python notebooks in the src folder.
Owner
- Name: Arne Diehl
- Login: adiehl96
- Kind: user
- Location: Nijmegen
- Repositories: 11
- Profile: https://github.com/adiehl96
Master student AI @ Radboud University, machine learning enthusiast, will hopefully automate my own job away