Q-Learning

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Enhancements ● ● ● Exploration strategy Store past state-action transitions and retrain on them periodically – The values may change as time progresses Temporal Difference <strong>Learning</strong>
Exploration Strategy ● ● ● Want to focus exploration on the good states Want to explore all states Solution: Randomly choose the next action – Give a higher probability to the actions that currently have better utility P a i ∣s= ∑ j k Qs , a i k Qs , a i
Page 1 and 2: Q-Learning ● ● ● Reinforcemen
Page 3 and 4: Our Problem ● ● ● ● ● We
Page 5 and 6: Formal Definition Q s , a=r s ,amax
Page 7 and 8: Example Problem a12 a23 s1 s2 s3 a2
Page 9 and 10: The Algorithm s1, a12 0 s1, a14 0 s
Page 11 and 12: Next Move s1, a12 0 s1, a14 0 s2, a
Page 13 and 14: Next Move s1, a12 0 s1, a14 0 s2, a
Page 15 and 16: New Game s1, a12 0 s1, a14 0 s2, a2
Page 17 and 18: Final State (after many iterations)
Page 19: Neural Network Approximation ●

Q-Learning

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