How AI Learns to Explore Better in Atari Games
Imagine teaching a game agent to be curious by giving small rewards when it sees something new.
This idea uses a predictive model that guesses what will happen next, and when the guess is wrong the agent gets an extra nudge.
Those nudges are called exploration bonuses and they push the AI to try places it might ignore otherwise.
The method works with raw screen pixels, so it learns straight from the game like a human watching the screen.
By training the model with a neural network the approach can handle big, messy game worlds, and it helped agents play many Atari games better than older tricks.
To show progress the team used a score summary called AUC-100, which compares how fast agents improve.
Results were clear: agents that get rewarded for curiosity find more useful strategies and get higher scores, even on games that were hard before.
It’s a simple idea, but it makes exploration feel more natural, and the agent learns new things faster than before.
Read article comprehensive review in Paperium.net:
Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models
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