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Deep Reinforcement Learning Applied to the Game of Qwixx
May 16 @ 6:00 pm - 8:00 pm
Co-sponsored by: CH06204 – San Diego Section Chapter, CIS11
A technical program starting at 6:30PM with refreshments and networking from 6:00 to 6:30.
Technical Abstract: Reinforcement learning methods have been steadily gaining popularity within the machine-learning community as an approach to learn gaming strategy through trial and error. One of the downsides of classical reinforcement learning is the limited applicability to games with a large state spaces which has resulted in the adoption of deep learning methods to approximate value functions defined over the state. However learning can still be difficult for complex games, particularly for those with a large stochastic component that can results in similar strategies having very different outcomes. In this work a deep reinforcement learning algorithm is presented to address these issues as seen in a representative dice based multi-player game known as Qwixx. Without knowing any rules about the game the algorithm eventually learns winning strategies by playing against a human generated method based on optimal control.
Speaker(s): Dr. Gideon Prior,
Advanced Test Equipment Rentals
10401 Roselle Street
San Diego, California