Markov Decision Process
A Markov Decision Process (MDP) is a mathematical framework for modeling sequential decision-making, defined by states, actions, transition probabilities, and rewards, used as the formal foundation of reinforcement learning.
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Glossary Terms(7)
Hidden Markov Model
A Hidden Markov Model (HMM) is a statistical model of a system that transitions between unobserved (hidden) states over time, where each hidden state produces…
Markov Decision Process
A Markov Decision Process (MDP) is a mathematical framework for modeling sequential decision-making, defined by states, actions, transition probabilities, and…
Bellman Equation
The Bellman equation expresses the value of being in a given state (or taking a given action) as the immediate reward plus the discounted value of whatever sta…
Policy Gradient
Policy gradient methods are a family of reinforcement learning algorithms that directly optimize a parameterized policy by estimating the gradient of expected…
Q-Learning
Q-learning is a model-free reinforcement learning algorithm that learns the value of taking a given action in a given state (the Q-value) through trial and err…
Actor-Critic Method
Actor-critic methods are a family of reinforcement learning algorithms that combine a policy network (the actor), which selects actions, with a value-function…
Monte Carlo Tree Search
Monte Carlo Tree Search (MCTS) is a heuristic search algorithm that builds a search tree by running randomized simulations, used to select strong actions in la…