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Probing Conceptual Knowledge on Solved Games's itch.io pageResults
| Criteria | Rank | Score* | Raw Score |
| ML Safety | #3 | 3.214 | 3.214 |
| Judge's choice | #4 | n/a | n/a |
| Reproducibility | #5 | 4.214 | 4.214 |
| Novelty | #13 | 2.857 | 2.857 |
| Interpretability | #14 | 2.929 | 2.929 |
| Generality | #20 | 2.286 | 2.286 |
Ranked from 14 ratings. Score is adjusted from raw score by the median number of ratings per game in the jam.
Where are you participating from?
["Online"]
What are the names of your team member?
Amir Sarid, Bary Levy, Dan Barzilay, Edo Arad, Itay Yona, Joey Geralnik
What is your team name?
mentaleap
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Comments
We explored how a Deep RL agent uses human interpretable concepts to solve connect-four.
Based on 'Acquisition of Chess Knowledge in AlphaZero' paper by DeepMind and Google Brain, we used TCAV to explore concepts detection in RL agent for connect four.
Our agent architecture was inspired by AlphaZero and trained using the OpenSpiel library by DeepMind.
Our novelty is in the decision to study connect four as it was solved with a knowledge based approach in 1988. Which means that to some extent we understand this game better than chess!