Q-learning is a model-free reinforcement learning algorithm used in machine learning. It is designed to enable an agent to learn a policy, which dictates its actions in an environment, in order to maximize a cumulative reward over time. Q-learning is particularly well-suited for problems where the environment is not known in advance and the agent must explore and learn through trial and error.
Q-learning has been successfully applied to a variety of problems, including robotics, game playing, and autonomous systems. It's a foundational algorithm in the field of reinforcement learning and serves as the basis for more advanced techniques and algorithms.
Key aspects:
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State (s): A representation of the current situation or configuration of the environment.
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Action (a): A decision or move that the agent can take in a given state.
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Reward (r): A numerical value associated with a specific action taken in a specific state. The goal of the agent is to maximize the cumulative reward over time.
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Q-value (Q): The expected cumulative future reward of taking action a in state s and following the optimal policy thereafter. The Q-value is updated iteratively as the agent interacts with the environment.
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Q-Table: A table that stores Q-values for all state-action pairs. The Q-table is initialized with arbitrary values and is updated through the learning process.
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Policy (π): A strategy or set of rules that the agent follows to decide which action to take in a given state. The optimal policy is the one that maximizes the expected cumulative reward.
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Exploration and Exploitation: Q-learning balances exploration (trying new actions to discover their effects) and exploitation (choosing actions that are known to yield high rewards based on current knowledge).
The Q-learning algorithm involves the following iterative steps:
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Initialization: Initialize the Q-table with arbitrary values.
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Exploration-Exploitation: Choose an action using an exploration strategy (e.g., epsilon-greedy policy).
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Observation and Reward: Observe the new state and the associated reward.
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Update Q-value: Update the Q-value of the chosen action in the current state based on the observed reward and the maximum Q-value of the next state.
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Repeat: Continue the process until convergence or a predetermined number of iterations.