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Reinforcement learning is a branch of machine learning that focuses on obtaining rewards by taking actions within an environment. It is often used to solve complex problems that require decision-making and planning. Common algorithms used in reinforcement learning include Q-learning, policy gradient methods, and evolutionary algorithms.
Q-Learning
Q-learning is an off-policy learning algorithm that uses a Q-table to store knowledge about the environment. It uses the Bellman equation to determine the best action to take in a given state based on the rewards and costs associated with it.
Policy Gradient Methods
Policy gradient methods learn a policy directly from the environment by maximizing the expected reward. These algorithms use a stochastic approach, meaning they are able to explore the environment without knowing the exact state of the system.
Evolutionary Algorithms
Evolutionary algorithms are inspired by natural selection and can be used to optimize a policy in a reinforcement learning environment. These algorithms use a population of policies that are evaluated against each other and then refined over time through mutation and crossover.
Q-Learning
Q-learning is an off-policy learning algorithm that uses a Q-table to store knowledge about the environment. It uses the Bellman equation to determine the best action to take in a given state based on the rewards and costs associated with it.
Policy Gradient Methods
Policy gradient methods learn a policy directly from the environment by maximizing the expected reward. These algorithms use a stochastic approach, meaning they are able to explore the environment without knowing the exact state of the system.
Evolutionary Algorithms
Evolutionary algorithms are inspired by natural selection and can be used to optimize a policy in a reinforcement learning environment. These algorithms use a population of policies that are evaluated against each other and then refined over time through mutation and crossover.