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DIAMOND (DIffusion As a Model Of eNvironment Dreams) is a reinforcement learning agent trained in a diffusion world model.

Home Page: https://arxiv.org/abs/2405.12399

License: MIT License

Shell 0.07% Python 99.93%
artifical-intelligense atari deep-learning diffusion-models machine-learning reinforcement-learning research world-models

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liunix61 utopic-dev ndevdrone xc0r fdoperezi evdcush ejboettcher zhikanggfu

diamond's Issues

About training time

Hi, thanks for such a great job!
You mentioned in your paper that "As a reference, unconstrained Atari agents are usually trained for 50 million steps" and "Each run utilized around 12GB of VRAM and took approximately 2.9 days on a single Nvidia RTX 4090".

I noticed that in your code, the outer loop that calls self.train_agent() is called 1000 times. The inner loop that trains the agent self.train_component(name, steps) is called 5000 times.
This is equivalent to the agent training 1000*5000 steps. The time corresponding to the inner loop is [496/5000 [12:59<1:09:31, 1.08it/s] about 1 hours.
So I guess the total time is about 1000 hours, and this is just for training agent model.
Did I overlook something ? And what should I do to reproduce the training time? @eloialonso @AdamJelley

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