'Q-Learning
is a special type of reinforcement learning which means learning by interacting
with an environment through positive feedback or reinforcement' - Siraj Raval
Jan 6, 2017
https://www.youtube.com/watch?v=A5eihauRQvo
'⟨s0,a0,r1,s1,a1,r2,s2,a2,r3,s3,a3,r4,s4...⟩, which means that the agent was in state s0 and did action a0, which resulted in it receiving reward r1 and being in state s1; then it did action a1, received reward r2, and ended up in state s2; then it did action a2, received reward r3, and ended up in state s3; and so on.' - David Poole and Alan Mackworth. 2010
http://artint.info/html/ArtInt_265.html
Q-learning is a type of algorithm that is designed to improve the AI over time or attempts, an example is if an AI was in a building and its goal is outside there would be a value for each room and its goal however each room along the way wold give a negative value until it reaches its goal. An AI with Q-learning will keep going through this process (trial and error) until it finds the optimal route/the highest reward.
https://www.youtube.com/watch?v=A5eihauRQvo
'⟨s0,a0,r1,s1,a1,r2,s2,a2,r3,s3,a3,r4,s4...⟩, which means that the agent was in state s0 and did action a0, which resulted in it receiving reward r1 and being in state s1; then it did action a1, received reward r2, and ended up in state s2; then it did action a2, received reward r3, and ended up in state s3; and so on.' - David Poole and Alan Mackworth. 2010
http://artint.info/html/ArtInt_265.html
Q-learning is a type of algorithm that is designed to improve the AI over time or attempts, an example is if an AI was in a building and its goal is outside there would be a value for each room and its goal however each room along the way wold give a negative value until it reaches its goal. An AI with Q-learning will keep going through this process (trial and error) until it finds the optimal route/the highest reward.
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