A transition graph model with utility and evidence counts learns behaviors from state history and feedback, showing performance comparable to neural networks on Atari Breakout.
Deploying Reinforcement Learning Approaches for Smart Home Automation , year=
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Interpretable experiential learning based on state history and global feedback
A transition graph model with utility and evidence counts learns behaviors from state history and feedback, showing performance comparable to neural networks on Atari Breakout.