Understanding Reinforcement Learning with Neural Networks Part 6: Completing the Reinforcement Learning Process
This article concludes the series on reinforcement learning with neural networks by detailing the training process. It explains how different input values affect the model's decision-making based on hunger levels. The article also summarizes the reinforcement learning process and hints at future topics to be covered.
- ▪The model is trained using input values between 0 and 1 to simulate different hunger levels.
- ▪When the input is 0.0, the model always chooses Place A, indicating low hunger.
- ▪Conversely, with an input of 1.0, the model always selects Place B, indicating high hunger.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 1207862) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Rijul Rajesh Posted on May 16 Understanding Reinforcement Learning with Neural Networks Part 6: Completing the Reinforcement Learning Process #ai #machinelearning In the previous article we covered the basics of training, and how rewards, derivatives and step-size were used to acheive it. In this article, we will finish the training process for the model. To fully train the model, we need to use different input values between 0 and 1 as inputs to the neural network.
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