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ljvmiranda921.github.io | ||
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www.neuralnet.ai
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iclr-blog-track.github.io
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| | | | | [AI summary] The provided text is an extensive blog post discussing the implementation and reproduction of the Proximal Policy Optimization (PPO) algorithm in various environments, including Atari, Procgen, and others. It highlights key implementation details, such as MultiDiscrete action spaces, vectorized environments, and accelerated training techniques like Envpool. The post also compares PPO with other algorithms like IMPALA and APPO, and emphasizes the importance of documentation and efficient code for reproducibility and research. | |
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www.v7labs.com
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| | | | | Deep reinforcement learning (DRL) combines reinforcement learning with deep learning. This guide covers the basics of DRL and how to use it. | |
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www.paepper.com
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| | | When you have a big data set and a complicated machine learning problem, chances are that training your model takes a couple of days even on a modern GPU. However, it is well-known that the cycle of having a new idea, implementing it and then verifying it should be as quick as possible. This is to ensure that you can efficiently test out new ideas. If you need to wait for a whole week for your training run, this becomes very inefficient. | ||
