Quantile Regression DQN a Minimal Working Example, Distributional Reinforcement Learning with Quantile Regression

Last update: Sep 17, 2022

Overview

Quantile Regression DQN

Quantile Regression DQN a Minimal Working Example, Distributional Reinforcement Learning with Quantile Regression (https://arxiv.org/abs/1710.10044).

Requirements (Python 3.6)

pip3 install gym, torch, numpy, tabulate, pandas

Credits

Joint work with Mikhail Konobeev. Of course, do not forget to cite Distributional Reinforcement Learning with Quantile Regression (https://arxiv.org/abs/1710.10044).

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Comments

loss calculation

Hello.

I have noticed that when loss is calculated there is detach() statement on diff:

loss = huber(diff) * (tau - (diff.detach() < 0).float()).abs()

could you please explain the reason?

Thanks a lot.

opened by merv22 4
Running error
Firstly, I run the rl_utils.py and it generate a qr-dqn-solution-cool.ipynb file. When I try to run the code in ipython notebook, the following error occured. Is there something wrong?

self.print = prin ^

SyntaxError: invalid syntax
opened by PygmalionChen 1
Visualization part

Hi, would you like to share the code for the img you used in gif format for each steps. It's a cool work, I would like to learn this, can you please share the code if possible.

opened by jahidhasanlinix 2

Owner

Arsenii Senya Ashukha

Research scientist at @SamsungLabs AI Center Moscow @bayesgroup

GitHub

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