OpenAI Blog · Dec 6, 2018
Quantifying generalization in reinforcement learning
Reviewed by Errol Vogt, Site support technician & online learning analyst · original summary · editorial policy
Quantifying generalization in reinforcement learning. We’re releasing CoinRun, a training environment which provides a metric for an agent’s ability to transfer its experience to novel situations and has already helped clarify a longstanding puzzle in reinforcement learning. CoinRun strikes a desirable balance in complexity: the environment is simpler than traditional platformer games like Sonic the Hedgehog but still poses a worthy generalization challenge for state of the art algorithms. This update is relevant for small-office operators tracking changes in their tools.
Operator takeaway: For operators: review whether 'Quantifying generalization in reinforcement learning' affects your current setup before relying on it in production.
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