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solution to cartpole problem of openAI gym with different approaches

License: MIT License

Jupyter Notebook 15.54% Python 84.46%
openai-gym cartpole-v0 keras dnn dqn reinforcement-learning

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cartpole-openai-gym's Issues

using frames to save history of m recent states

Good work!

1- How this code chanes if we want to use frames to save history of m recent states (For instance m=4 as presented in nature paper Mnih et al. 2015 )?

2- Saving model is similar to save weight (because the numbers of nodes are constant). Is there anything more? Why is made your code more accurate than the code of Keon?

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