Reinforcement Learning
"Reinforcement learning is learning what to do – how to map situations to actions – so as to maximize a numerical reward signal. The learner is not told which actions to take, as in most forms of machine learning, but instead must discover which actions yield the most reward by trying them."
– Sutton, Richard S. and Andrew G. Barto. Reinforcement Learning: An Introduction. (1.1). MIT Press, Cambridge, MA, 1998.
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Inherently Explainable Reinforcement Learning in Natural Language
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Observation: Up a tree Beside you on the branch is a small birds nest In the birds nest is a large egg encrusted with precious jewels, scavenged by a childless songbird... Explanation: I am in the Forest Path now.
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Flexible Attention-Based Multi-Policy Fusion for Efficient Deep Reinforcement Learning
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Humans are great observers who can learn by aggregating external knowledge from various sources, including observations from others' policies of attempting a task.
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