Epsilon-Greedy Algorithm in Reinforcement Learning - GeeksforGeeks

In Reinforcement Learning, the agent or decision-maker learns what to do—how to map situations to actions—so as to maximize a numerical reward signal. The agent is not explicitly told which actions to take, but instead must discover which action yields the most reward through trial and error. Multi-Armed Bandit Problem The multi-armed bandit problem is used in reinforcement learning to formalize the notion of decision-making under uncertainty. In a multi-armed bandit problem, an agent(learner) chooses between k different actions and receives a reward based on the chosen action. The multi-armed bandits are also used to describe fundamental concepts in reinforcement learning, such as rewards, timesteps, and values. For selecting an action by an agent, we assume that each action has a separate distribution of rewards and there is at least one action that generates maximum numerical reward. Thus, the probability distribution of the rewards corresponding to each action is different and is u

Epsilon-Greedy Algorithm in Reinforcement Learning - GeeksforGeeks Courses Tutorials Interview Prep Python for Machine Learning Machine Learning with R Machine Learning Algorithms EDA Math for Machine Learning Machine Learning Interview Questions ML Projects Deep Learning NLP Computer vision Data Science Artificial Intelligence Epsilon-Greedy Algorithm in Reinforcement Learning Last Updated : 10 Jan, 2023 In Reinforcement Learning, the agent or decision-maker learns what to do—how to map situations to actions—so as to maximize a numerical reward signal. The agent is not explicitly told which a

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