Retraining the saved model about reinforcement-learning-algorithms HOT 5 CLOSED

@anushmanukyan Could I know which environment and algorithm you are training for?

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@TianhongDai I am using PPO.

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@anushmanukyan I guess you just load the weights of the model. if you check the line here: https://github.com/TianhongDai/reinforcement-learning-algorithms/blob/master/07-proximal-policy-optimization/ppo_agent.py#L113 When I test the network, I also load the object of running mean filter. Because during training , I use the running mean filter to normalize the input. So, if you want to retrain your model, you should also load the "trained" running mean filter. Otherwise you will get different result.

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I added running mean filter and retraining seems to work better now.
However I have another question: how the demo.py works? Basically I can not figure out how the testing works, since I save the best model, but then when i test this model it has different reward than it had while saving that model. How it can be possible? And also if I run several times the same model then I get different performance.

Thank you so much for your help.

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@anushmanukyan Hi, I think demo.py should work fine, you can download my pre-trained model from: https://drive.google.com/drive/u/2/folders/1cZjjCA5WHs-Lfw63ntzeUjMo_wZoIgXw Then, just run python demo.py . It will still get same high scores as it get during training. You can check https://github.com/TianhongDai/reinforcement-learning-algorithms/blob/master/07-proximal-policy-optimization/ppo_agent.py#L111 here to see how did i test the network.

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