A.I. Learns to DRIVE
Code Bullet・2 minutes read
The author explores advanced training techniques like deep q-learning and PPO, which influences a transformation in his coding style. By integrating neural networks into q-learning, the AI in a car driving game learns to navigate through echolocation-like lines, facing challenges but ultimately mastering skills like Tokyo drifting for maximum rewards.
Insights
- Q-learning is a reinforcement learning method where AI is rewarded for positive actions and penalized for negative ones, illustrated through a mouse learning to avoid cats and find cheese.
- The integration of neural networks into q-learning enhances AI capabilities, as seen in a top-down car driving game where AI learns through reward gates and points, eventually mastering complex maneuvers like Tokyo drifting after overnight training.
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Recent questions
What is q-learning?
A method rewarding AI for positive actions.
How does collision detection work in games?
Cars must stay on track to avoid failure.
What is the purpose of echolocation-like lines in AI perception?
To help AI perceive the environment.
How are neural networks integrated into q-learning?
For more advanced AI capabilities.
What challenges are faced when switching programming languages for AI implementation?
Learning new libraries and tools.
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