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yingzwang/DeepQ-cartpole.ipynb

Last active July 8, 2022 08:29
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Deep-Q learning implementation in Tensorflow and Keras (solving CartPole-v0)
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DeepQ-cartpole.ipynb
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@yingzwang
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dqn
DQN graph (generated by tensorboard)

@DylanHaiyangChen
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Hi Ying,
This is a really great approach to solve CartPole problem. I wonder if you would like to support more information about the DQN architecture. Such like report or references.
I am thinking about why your implement is of high efficiency.

@TomeASilva
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Hi Ying,
This is a really great approach to solve CartPole problem. I wonder if you would like to support more information about the DQN architecture. Such like report or references.
I am thinking about why your implement is of high efficiency.

Hi dylan, HaiyangChen

I'm not associated with yingzwang, but i can give some information, this an implementation of DQN algorithm ( https://deepmind.com/research/dqn/. ) So, the architecture of the algorithm is essentially the same as the one presented in the paper. The difference is a soft-update to the weights of the target network by using exponential moving averages parameterized by tau. She also uses a decreasing exploration strategy, which clearly helps in this problem. The rest is just good hyper parameter tunning.

The code is also very good, good code practices all around.

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