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--- |
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tags: |
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- SuperMarioBros-v0 |
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- RND |
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- CNN |
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- reinforcement-learning |
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- custom-implementation |
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- atari |
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- SuperMarioBros-v0 |
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model-index: |
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- name: RND-SuperMarioBros-v0 |
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results: |
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- task: |
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type: reinforcement-learning |
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name: reinforcement-learning |
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dataset: |
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name: SuperMarioBros-v0 |
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type: SuperMarioBros-v0 |
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metrics: |
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- type: mean_reward |
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value: 2502.70 +/- 418.86 |
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name: mean_reward |
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verified: false |
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--- |
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# **RND with CNN** Agent playing **SuperMarioBros-v0** |
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This is a trained model of a **RND-CNN** agent playing **SuperMarioBros-v0** . |
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To learn to use this model and train yours check this notebook on kaggle: https://www.kaggle.com/code/syedjarullahhisham/drl-extra-personal-unit-5-rnd-super-mario-bros |
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## Codes |
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Github repos(Give a star if found useful): |
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* https://github.com/hishamcse/DRL-Renegades-Game-Bots |
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* https://github.com/hishamcse/Advanced-DRL-Renegades-Game-Bots |
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* https://github.com/hishamcse/Robo-Chess |
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Kaggle Notebook: |
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* https://www.kaggle.com/code/syedjarullahhisham/drl-extra-personal-unit-5-rnd-super-mario-bros |
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* https://www.kaggle.com/code/syedjarullahhisham/drl-extra-personal-unit-5-rnd-montezuma-mario-bros |
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# HyperParameters: |
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``` |
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"trainmethod": "RND", |
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"envid": "SuperMarioBros-v0", |
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"maxstepperepisode": 18000, |
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"learningrate": 0.0001, |
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"numenv": 128, |
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"numstep": 128, |
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"gamma": 0.999, |
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"intgamma": 0.99, |
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"lambda": 0.95, |
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"usegae": true, |
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"clipgradnorm": 0.5, |
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"entropy": 0.001, |
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"epoch": 4, |
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"minibatch": 4, |
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"ppoeps": 0.1, |
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"extcoef": 5.0, |
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"intcoef": 1.0, |
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"stickyaction": true, |
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"actionprob": 0.25, |
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"lifedone": false, |
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"obsnormstep": 50 |
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``` |
