Robust Decision Making Via Free Energy Minimization скачать в хорошем качестве

Robust Decision Making Via Free Energy Minimization

Link to Arxiv Paper: https://arxiv.org/abs/2503.13223 Link to Colab Notebook: https://colab.research.google.com/dri... Link to Google Doc: https://docs.google.com/document/d/1O... This video discusses a paper on robust decision-making via free energy minimization and introduces a method called Dr. Free, which incorporates free energy minimization principles into AI reinforcement learning [00:03]. Key points covered in the video: Probability Theory in AI: The video explains the two main types of probability algorithms used in AI: KV (Kack leer variational) and Monte Carlo [01:43]. KV approximates interference via optimization, while Monte Carlo approximates inference via sampling [02:05]. Dr. Free Method: This method relies on distance measurements, similar to the k-nearest neighbors concept, but for reinforcement learning [02:54]. It involves creating different environmental scenarios and training the model to predict based on the most equivalent scenario [03:18]. KV vs. Monte Carlo: KV transforms an inference problem into an optimization problem, while Monte Carlo transforms it into a sampling problem [04:37]. Free Energy Frameworks: KV appears naturally in variational free energy formulations, while Monte Carlo appears in approximate valuation of expectations [05:06]. Gradient Descent and Free Energy Principles: The paper links gradient descent to free energy principles [06:24]. Bayesian Belief Updating: Dr. Free utilizes Bayesian belief updating, which is considered important for how logic works [07:17]. Code Implementation: The video shows the code for the Dr. Free method, including a prototype model and a full implementation [07:57]. Addition of Memory: The presenter adds a memory unit to the Dr. Free framework to enhance the model's capabilities and reduce computational costs [12:21]. Environmental Learning: The presenter emphasizes the importance of environmental learning for AI models, stating that the more robust the environments and the more ability to learn directly from the environment, the better the model's performance [14:34]. AI and Physics: The presenter argues that AI models are not an exception to the rules of physics and that they utilize free energy minimization and care about energy conservation [15:40].