Q-Learning Network MLM Flashcards

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1
Q

Q-Learning

A

Q-Learning is a model-free reinforcement learning algorithm. The goal of Q-learning is to learn a policy, which tells an agent what action to take under what circumstances.

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2
Q
  1. Introduction
A

Q-learning is a values iteration algorithm in reinforcement learning. It’s used to learn the optimal policy for a Markov Decision Process (MDP) when the transition model is not known. The policy learned is the one that maximizes the total reward over all successive steps.

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3
Q
  1. Action-Value Function
A

In Q-Learning, the value of a state-action pair is represented by a Q-value, stored in a Q-table. The Q-value is a measure of the expected return from a state, given an action and following a specific policy.

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4
Q
  1. Q-table
A

The Q-table is a table of states and actions that guides the agent to the best action from a given state. The table is initialized arbitrarily, and then values are updated iteratively based on the reward received for actions taken.

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5
Q
  1. Learning Process
A

During the learning process, the agent explores the environment, and the Q-values are updated using the Bellman equation. This equation states that the Q-value for a state-action pair is the immediate reward plus the discounted maximum Q-value for the next state.

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6
Q
  1. Exploitation vs Exploration
A

The agent needs to balance exploration (trying out new actions to see their effect) and exploitation (choosing the action with the highest Q-value). This is often managed with an ε-greedy strategy, where the agent chooses a random action with probability ε and the action with the highest estimated reward with probability 1-ε.

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7
Q
  1. Convergence
A

Under certain conditions, the Q-learning algorithm is guaranteed to converge to the optimal policy. These conditions include having a finite number of states and actions, and each state-action pair being visited an infinite number of times.

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8
Q
  1. Advantages
A

Q-Learning is a model-free approach, meaning it can learn optimal actions just from interactions with the environment without needing a model of the environment’s dynamics. It can handle problems with stochastic transitions and rewards without requiring adaptations.

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9
Q
  1. Applications
A

Q-Learning has been used successfully in various domains including robotics, scheduling, gaming, and resource management.

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