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- Mean Reward Description: The average reward in the context of reinforcement learning refers to the average amount of reward an agent receives over time(...) Read more
- Max-Q Description: Max-Q is a hierarchical reinforcement learning algorithm that focuses on breaking down the value function into smaller, manageable(...) Read more
- Model Predictive Control Description: Model Predictive Control (MPC) is an advanced process control method that uses a mathematical model of the system to be controlled(...) Read more
- Multi-Agent Reinforcement Learning Description: Multi-Agent Reinforcement Learning (MARL) is an approach within reinforcement learning where multiple agents interact with each(...) Read more
- Markov Blanket Description: Markov Blanket is a fundamental concept in the field of reinforcement learning and graph theory. It refers to a set of nodes in a(...) Read more
- 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
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