On the Road to Artificial General Intelligence
summary
Game engine environments (Unity and the Unity ML-Agents toolkit) host reinforcement learning at scale: pixels-in, actions-out agents learn tabula rasa behaviors like a chicken avoiding cars in about six hours and four‑legged agents learning fetch after many training episodes. Curriculum learning and LSTM memory enable tool use and color-based navigation without handcoding. Adding a curiosity intrinsic reward drops discovery from millions of random trials to roughly 10–20k attempts. Multiagent teams show emergent goalie and passing behavior when rewards are shared.
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chapters (13)
- 0:00Introduction
- 1:28About Unity Technologies
- 2:18What is Artificial Intelligence?
- 6:22ML Training Environments
- 7:51Nature's Learning Method: Reinforcement
- 8:43Chicken Crossing the Road: Tabula Rasa
- 13:49Enabling Long Short-Term Memory (LSTM)
- 15:28Cooperative Rewards
- 15:46Competive Rewards
- 21:37Limits to Standard Reinforcement Learning
- 25:16The Good Puppy, Bad Puppy Method
- 29:02ML-Agents Training Environments
- 33:43From Biological to Cultural Evolution
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