QLearning
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 error, enabling an agent to derive an optimal policy without knowing the environment's dynamics in advance.
6 resources across 1 library
Glossary Terms(6)
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…