Q-Learning

More documents

Recommendations

Info

Reinforcement <strong>Learning</strong> ● ● ● ● ● Delayed reward – We don't immediately know whether we did the correct thing Encourages exploration We don't necessarily know the precise results of our actions before we do them We don't necessarily know all about the current state Life-long learning
Our Problem ● ● ● ● ● We don't immediately know how beneficial our last move was Rewards: 100 for win, -100 for loss We don't know what the new state will be from an action Current state is well defined Life-long learning?
Page 1: Q-Learning ● ● ● Reinforcemen
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 and 20: Neural Network Approximation ●
Page 21 and 22: Exploration Strategy ● ● ● Wa

Q-Learning

Create successful ePaper yourself

Delete template?

Save as template?