Q-Table Concept
Table-based reinforcement learning.
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Q-Table Concept
The Q-Table is a core concept in reinforcement learning. It is a lookup table where each entry represents the expected reward for taking a specific action in a given state.
The goal is to learn the Q-values, which guide the agent to take the best actions for maximum rewards.
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Structure of a Q-Table
The Q-Table is structured as:
- Rows: Represent states of the environment.
- Columns: Represent actions available to the agent.
- Values: Represent the Q-value for state-action pairs.
The agent updates these values during learning.