arXiv:1606.04460stat.ML
Model-Free Episodic Control
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Abstract
State of the art deep reinforcement learning algorithms take many millions of interactions to attain human-level performance. Humans, on the other hand, can very quickly exploit highly rewarding nuances of an environment upon first discovery. In the brain, such rapid learning is thought to depend on the hippocampus and its capacity for episodic memory. Here we investigate whether a simple model of hippocampal episodic control can learn to solve difficult sequential decision-making tasks. We demonstrate that it not only attains a highly rewarding strategy significantly faster than state-of-the-art deep reinforcement learning algorithms, but also achieves a higher overall reward on some of the more challenging domains.
Yes. In Chess, it's relatively easy to judge how good a board position is, and thus people have been successful by hand-engineering the board position evaluator (also called value function in RL lingo), and then just doing tree search to take the action which improves board…
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