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Black-box, gradient-free optimization of car-racing policies.

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racer

Black-box, gradient-free optimization of car-racing policies.

Example Race

Our best performing agent, the tuned genetic program (red) races against the untuned genetic program (blue). demo

Inference Pipeline

overview

Results

Method Name Max Reward Mean Reward Mean # Function evaluations to reach 900
Nelder Mead 713.2 Not Applicable Not Applicable
NN + Generation-based Evolution Strategy (3 repetitions) 925.7 923.3 9.8k
NN + Iterative Evolution Strategy (4 repetitions) 915.5 906.3 50.3k
Genetic Program (4 repetitions) 928.2 917.2 5.8k
Tuned Genetic Programming 930.6 Not Applicable Not Applicable

Our best results were achieved by fine tuning constants of the best genetic program using evolution strategies.

Environment

Graphics, physics engine and reward calculation adapted from OpenAI gym.

To improve performance, we rewrote the graphics pipeline yielding ~40x sequential speedup. Our modifications allow the evaluations to run headless and be parallelized. We also added a feature where multiple agents are evaluated simultaneously, allowing for a "race" to be visualized. All experiments were performed on the ETH Euler Supercomputer.

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Black-box, gradient-free optimization of car-racing policies.

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