Hot-Or-Not

A javascript emulator inspired by Data Aging use case. We proved - though simplistically - that we could potentially let an RL agent learn a repeated workload to place data in hot and cold partition just by designing a rewarding process.

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In-Mem DB Tuning PoC

To test technical feasibility, we deploy an RL agent tune a single In-Mem DB parameter (cpu concurrency) to optimize the performance of a particular workload. Our RL Agent can successfully learn that it does not need all CPU in the system in order to serve the query

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Pong

A game of Pong based on JavaScript that pits RL player vs original computer player. The RL agent rewarded only when it successfully score a goal at his opponent, and be punished when it lose the ball.

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Breakout

This our first game and because our rewarding design (just hovering the ball) RL Agent learns a creative way to earn endless reward. In the world of RL, be careful of your rewarding design

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Tic-Tac-Toe

Deep Reinforcement Learning Agent learnt from first experience including getting penalized for place X on top of O. Eventually, it learns the rules of the game and start winning or draw with the random Agent

Coming Soon »


A short introduction on what is Deep Reinforcement Learning

Check out our updated interactive slide


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