Double DQN miserable attempt

[theomarzaki]

@J4BB3R unfortunately I cannot see the branch you are working on,

But I would start of this way: Pull latest master Copy all dueling dqn page into a new page Change the model architecture to fit double dqn Refactor as necessary

Hope this helps :)

[turbokadi]

@theomarzaki I've forgot to push my branch, it's on my way.

[theomarzaki]

Hello @J4BB3R ,

I took a look at the branch, and everything seems to be coming together:

I have fixed the bugs in the file (check commit diff to see the full details) - in summary :

• The model needs access to the fields such as learning rate and mini batch size ( adding a parser will really clean up the implementation but you need to assign the values taken from the parser to the model values itself)

• The model Is now incorporated in the environment (adding Agent, giving the model the data .... ) so training can occur on the data set

• Target Network has been incorporated in training (passed as parameter to stabilise training) -> could and should be changed to see effects (optimality)

Although it is ready to train now: I would take a deeper look at the actual Double DQN architecture itself (I suggest trying something along the lines of Double DQN Pytorch (GitHub) to get started)

Other than that I think all is well :) !

[turbokadi]

Ok I've looked the modifications but I don't understand why you would need values in the model, the parent need those values ? My implementation is not really an implementation because it's just your DQN. I'm currently on an pytorch Double QN example to adapt at our case.

Have a nice day :)

[theomarzaki]

I thought from an OOP perspective it would be a simple case of having all the models in the same file later (after all the research has been completed) , as they all use the same env simulation etc ... for a cleaner struct.

I think it is worth to merge master into your branch as I continuously update the environment methods etc to create a more stable training environment so the models converge faster.

Thank you very much, Likewise !