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One option worth exploring is leveraging the implementation of RLlib, as it inherently supports multi-GPU training. RLlib provides a robust framework for reinforcement learning, and its multi-GPU capabilities can serve as a valuable reference point for your codebase extension.
Moreover, if you can accept TF implementation, you might find DreamerV3 within the RLlib library.
Hi,
How to extend the codebase to multi-gpus ? Seems like non-trivial to do that?
Thank you,
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