Forgetting in function approximation

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Hello everyone

I am currently spending some time on writing DDPG algorithm for continuous control. I run this algo on lunar lander environment with actor lr 1e_4 and critic lr 1e_3. It learns how to land but at some point -like 40 consecutive successful episodes- unfortunately it starts to forget. I know that it is in nature of function approximation. We lose GPI with function approximation. Is there any way to mitigate this forgetting? I use ADAM optimizer in traninig process. I use gaussian noise to prompt exploration. Should I decrease noise std or learning rates of NN s by time?

Thank you in advance

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massagre

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Posted: 4 years ago
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External URL: reddit.com/r/reinforceme...