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Neural networks made easy (Part 38): Self-Supervised Exploration via Disagreement

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by , 03-14-2024 at 04:18 AM (200 Views)
      
   
This algorithm is based on a self-learning method, where the agent uses information obtained during interaction with the environment to generate "intrinsic" rewards and update its strategy. The algorithm is based on the use of several agent models that interact with the environment and generate various predictions. If the models disagree, it is considered an "interesting" event and the agent is incentivized to explore that space of the environment. In this way, the algorithm incentivizes the agent to explore new areas of the environment and allows it to make more accurate predictions about future rewards.
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