Q-Learning — AI Learning Through Trial and Error — Free AI & ML Tutorial
Learn Q-Learning — AI Learning Through Trial and Error in AI & ML with a free, beginner-friendly tutorial, examples and practice for Indian students on Syllab.in.
TL;DR: Learn Q-Learning — AI Learning Through Trial and Error in AI & ML with a free, beginner-friendly tutorial, examples and practice for Indian students o…
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Q-Learning — AI Learning Through Trial and Error in AI & ML
Q-Learning is a fundamental Reinforcement Learning algorithm. "Q" = Quality of an action. The agent learns Q(state, action) = expected future reward for taking that action in that state.
The agent explores the environment, receives rewards/penalties, and updates its Q-values. Over time, it learns the optimal policy (which action to take in each state).
Exploration vs Exploitation: epsilon-greedy strategy randomly explores (try new actions) with probability ε, and exploits (choose best known action) with probability 1-ε.
Applications: Game-playing (AlphaGo), robotics (learning to walk), autonomous vehicles, optimal control, resource allocation.
Q-Learning — AI Learning Through Trial and Error — Syntax
# Q-Learning update rule: # Q(s, a) ← Q(s, a) + α[r + γ·max Q(s', a') - Q(s, a)] # where: # α = learning rate # γ = discount factor (how much future rewards matter) # r = immediate reward # s' = next state
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