deep-q-learning
pytorch-learn-reinforcement-learning
deep-q-learning | pytorch-learn-reinforcement-learning | |
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1 | 3 | |
1,209 | 139 | |
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0.0 | 0.0 | |
over 3 years ago | almost 3 years ago | |
Python | Python | |
MIT License | MIT License |
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deep-q-learning
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Deep Q Network knapsack problem
So go online on GitHub and find a DQN implementation that has options for using a feedforward net as input (instead of conv net as your input isn’t pixel based). Any remotely modular piece of code will take in state space size and action space as parameters to their NN. This is essentially setting input layer to be equal to state space (so 4) and output layer to be action space (201). (https://github.com/keon/deep-q-learning) this repo seems helpful i a cursory glance
pytorch-learn-reinforcement-learning
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