DQN/DDQN-Pytorch
This is a clean and robust Pytorch implementation of DQN and Double DQN. Here is the training curve:
All the experiments are trained with same hyperparameters.
A quick render here:
Dependencies
gym==0.18.3
numpy==1.21.2
pytorch==1.8.1
How to use my code
Train from scratch
run ‘python main.py’, where the default enviroment is CartPole-v1.
Play with trained model
run ‘python main.py –write False –render True –Loadmodel True –ModelIdex 50000’
Change Enviroment
If you want to train on different enviroments, just run ‘python main.py –EnvIdex 1’.
The –EnvIdex can be set to be 0 and 1, where
‘–EnvIdex 0’ for ‘CartPole-v1’
‘–EnvIdex 1’ for ‘LunarLander-v2’
Visualize the training curve
You can use the tensorboard to visualize the training curve. History training curve is saved at ‘\runs’
Hyperparameter Setting
For more details of Hyperparameter Setting, please check ‘main.py’
References
DQN: Mnih V , Kavukcuoglu K , Silver D , et al. Playing Atari with Deep Reinforcement Learning[J]. Computer Science, 2013.
Double DQN: Hasselt H V , Guez A , Silver D . Deep Reinforcement Learning with Double Q-learning[J]. Computer ence, 2015.
