A.I. Learns to DRIVE
Code Bullet・19 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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Summary
00:00
Exploring Advanced AI Techniques in Game Development
- The text discusses the author's journey into learning about advanced training techniques like deep q-learning and PPO.
- The author describes a transformation in his coding style, influenced by new techniques he's exploring.
- The author introduces the concept of q-learning, a method that rewards AI for positive actions and punishes for negative ones.
- An example of q-learning is provided with a mouse learning to avoid cats and find cheese.
- The author transitions to creating a top-down car driving game, starting with basic movement mechanics.
- Collision detection is explained as the car needing to stay on the track to avoid failure.
- The author details how the AI in the game will perceive the environment using echolocation-like lines.
- The addition of drifting mechanics to the game is discussed and demonstrated.
- The author delves into the integration of neural networks into q-learning for more advanced AI capabilities.
- The challenges faced in switching from JavaScript to Python for implementing the AI are highlighted, including the need to learn new libraries and tools.
11:56
AI learns to Tokyo drift for rewards
- The AI is trained using reward gates and points along a track to incentivize movement and progress.
- After 800 games, the AI can consistently turn right, but struggles with sharp corners.
- Following overnight training, the AI surprises with advanced skills, including Tokyo drifting to maximize rewards.
- A daily problem-solving platform, Brilliant.org, is recommended for logical thinking challenges and learning opportunities, with a discount for early sign-ups.
