Reinforcement Learning Progress
Summary
We used PPO (Proximal Policy Optimization), a general reinforcement learning algorithm invented by OpenAI, to train a team of 5 agents to play Dota and beat semi-pros. This is the game that to me feels closest to the real world and complex decision making (combining strategy, tactics, coordinating, and real-time action) of any game AI had made real progress against so far. This is a big deal because it shows that deep reinforcement learning can solve extremely hard problems whenever you can throw enough computing scale and a really good simulated environment that captures the problem you’re solving. Its easy to imagine this being applied to environments that look increasingly like the real world. I expect this to be a large branch of machine learning, and an important step on the road towards general intelligence.
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