Overview of Deep Reinforcement Learning Methods - presented by Prof. Steven L. Brunton

Overview of Deep Reinforcement Learning Methods

Prof. Steven L. Brunton

Prof. Steven L. Brunton
Slide at 16:32
Video Pinball
2539%
Boxing
1707%
Breakout
1327%
Star Gunner
598%
Robotank
508%
Atlantis
449%
Crazy Climber
419%
Gopher
400%
Demon Attack
294%
Name This Game
278%
Krull
277%
Assault
246%
Road Runner
232%
Kangaroo
224%
James Bond
145%
Tennis
143%
Pong
132%
Space Invaders
121%
Beam Rider
119%
Tutankham
112%
Kung-Fu Master
102%
Freeway
102%
Time Pilot
100%
Enduro
Fishing Derby
Up and Down
Ice Hockey
Q*bert
H.E.R.O.
At human-level or above
Asterix
Below human-level
Battle Zone
Wizard of Wor
Chopper Command
Centipede
Bank Heist
River Raid
Zaxxon
Amidar
Alien
Venture
Seaquest
Double Dunk
Bowling
Ms. Pac-Man
Asteroids
Frostbite
Gravitar
Private Eye
Montezuma's Revenge
600 1,000
4,500%
Share slide
Summary (AI generated)

Okay, so that was a discussion on deep Q learning. Essentially, one can use the traditional Q-learning method and create a loss function for the neural network. Through trial and error and experience, the neural network will learn how to analyze the data to provide the best Q function possible.