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- Multi-Objective Reinforcement Learning Description: Multi-objective Reinforcement Learning is an approach within reinforcement learning that focuses on optimizing multiple objectives(...) Read more
- Markov Decision Process with Function Approximation Description: The Markov Decision Process with Function Approximation is an extension of Markov Decision Processes (MDP) that allows addressing(...) Read more
- Maximum Likelihood Estimation Description: Maximum Likelihood Estimation (MLE) is a statistical method used to estimate the parameters of a model based on a set of observed(...) Read more
- Modular Learning Description: Modular learning is an educational approach that divides the learning process into distinct modules or components, allowing(...) Read more
- Meta-Policy Description: Meta-policy in the context of reinforcement learning refers to a policy that guides the learning of other policies. In this(...) Read more
- Markov Random Field Description: The Markov Random Field (MRF) is a probabilistic model that represents dependencies between random variables in a graphical(...) Read more
- Modeling Error Description: The 'Modeling Error' in the context of reinforcement learning refers to the discrepancy between the results predicted by a model(...) Read more
- MDP Description: The Markov Decision Process (MDP) is a mathematical framework used to model decisions in situations where outcomes are partly(...) Read more
- Mean Field Reinforcement Learning Description: Mean Field Reinforcement Learning is a variant of reinforcement learning that focuses on analyzing the interactions between(...) Read more
- Manipulation Description: Manipulation in Generative Adversarial Networks (GANs) refers to the process of altering generated data to achieve desired(...) Read more
- Monotonic Description: In the context of Generative Adversarial Networks (GANs), the term 'monotonic' refers to functions that maintain a consistent(...) Read more
- Monocular Description: Monocular GANs are a type of generative adversarial networks that specialize in generating data from a single viewpoint. These(...) Read more
- MaxPooling Description: MaxPooling is a subsampling operation used in convolutional neural networks (CNNs) that aims to reduce the dimensionality of the(...) Read more
- MeanPooling Description: MeanPooling is a subsampling operation used in convolutional neural networks that calculates the average value of a set of values(...) Read more
- Multi-head Attention Description: Multi-head attention is a fundamental mechanism in large language models and convolutional neural networks, allowing the model to(...) Read more
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