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- Principle of Least Astonishment Description: The Principle of Least Astonishment is a fundamental concept in interface and system design, especially in the realm of user(...) Read more
- Predictive Accuracy Description: Predictive accuracy refers to the degree to which predictions made by a statistical or artificial intelligence model align with the(...) Read more
- Pragmatic Approaches Description: Pragmatic approaches in explainable artificial intelligence (XAI) refer to practical methods used to enhance the interpretability(...) Read more
- Privacy Technology Description: Privacy technology refers to tools and systems designed to protect users' personal data, ensuring that sensitive information is not(...) Read more
- Policy Improvement Description: Policy improvement in the context of reinforcement learning refers to the process of adjusting a policy, which is a strategy that(...) Read more
- Policy Iteration Description: Policy iteration is a fundamental approach in reinforcement learning that focuses on optimizing policies through a cyclical(...) Read more
- Policy Search Description: Policy search is an approach within reinforcement learning that focuses on finding the best possible policy for an agent in a given(...) Read more
- Policy-Based Methods Description: Policy-Based Methods are a category within reinforcement learning that focuses on the direct optimization of the policy, that is,(...) Read more
- Pseudoreward Description: Pseudoreward is a concept in the field of reinforcement learning that refers to a reward signal that does not represent the actual(...) Read more
- Policy Regularization Description: Policy regularization is a fundamental technique in the field of reinforcement learning, designed to prevent overfitting of an(...) Read more
- Prioritized Experience Replay Description: Prioritized Experience Replay is a technique within reinforcement learning that focuses on selecting past experiences based on(...) Read more
- Policy Evaluation Algorithm Description: The Policy Evaluation Algorithm is a fundamental technique in the field of reinforcement learning, used to calculate the value(...) Read more
- Policy Gradient Theorem Description: The Policy Gradient Theorem is a fundamental concept in the field of reinforcement learning that allows for the calculation of the(...) Read more
- Potential Function Description: The potential function is a fundamental concept in the field of reinforcement learning, used to define the potential of states in a(...) Read more
- Policy Convergence Description: Policy convergence in the context of reinforcement learning refers to the condition where a policy, that is, a strategy that an(...) Read more