RainBow
The OpenAI Gym can be paralleled by the bathEnv.py, which makes the training faster.
You can use the following command to choose which DQN to use:
python main.py --is_double 1 --is_duel 1 --is_per 1 --is_distributional 1 --is_noisy 1 --num_step 3
The output looks like:
Number_of_frame mean_max_Q average_reward variance_reward
RainBow:
https://arxiv.org/abs/1710.02298
DQN done:
https://web.stanford.edu/class/psych209/Readings/MnihEtAlHassibis15NatureControlDeepRL.pdf
Double DQN done:
https://arxiv.org/abs/1509.06461
Duel DQN done:
https://arxiv.org/abs/1511.06581
DQN, Double DQN, and Duel DQN parts are implemented by Ben and me. https://github.com/bparr/10703
PER DQN done:
https://arxiv.org/abs/1511.05952
Distributional DQN done:
https://arxiv.org/abs/1707.06887
The best and most concise implement of Distributional RL loss function in Tensorflow by far in the world!!! Better and faster than all others' implements that I can find.
Multi-step done
NoisyNet done
https://arxiv.org/pdf/1706.10295.pdf
Thanks to: https://github.com/andrewliao11/NoisyNet-DQN/blob/master/tf_util.py