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License: MIT License
An implementation of Deep Q-Learning from Demonstrations (DQfD) for playing Atari 2600 video games
License: MIT License
Hi, I am running this on a personal computer with a 3070ti and 32gb of ram.
I am immediately running out of memory with a error saying it can't allocate 9gb of memory.
What are the system requirements for this or do I need to optimise my setup? I am using the packages and versions in requirements.txt.
Thanks.
I use pytorch to implement the DQfD as well, for pure DQN, the result seems to meet expectations. however, when I use DQfD, after the pretrain process, the score result becomes worse. I only use the Je loss .
My strategy is first pretrain, then set epsilon to 1 to random explore and decrease 0.001 every game step. which means, after pretrain on demonstration data, the subsequent process is the normal DQN.
Can you give me some suggestion about it.
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