The bots in the simulation have simple brains (neural nets) that control their movements. Input to the brains: bot's head position in the world space and angles of bot's leg and knee joints; output of the brains: rotation forces to bot's leg and knee joints. After every iteration, a fitness function chooses the most fit bots to the next iteration (fitness score of bot: how much the bot traveled to the right + whether bot fell down or not).
Two variations of neuroevolution are available:
- NEAT (network architecture and weights are updated)
- Vanilla Neuroevolution (only network weights are updated)
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