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Reinforcement Learning with Feedback Graphs
[article]
2020
arXiv
pre-print
We study episodic reinforcement learning in Markov decision processes when the agent receives additional feedback per step in the form of several transition observations. Such additional observations are available in a range of tasks through extended sensors or prior knowledge about the environment (e.g., when certain actions yield similar outcome). We formalize this setting using a feedback graph over state-action pairs and show that model-based algorithms can leverage the additional feedback
arXiv:2005.03789v1
fatcat:iyfcslvyqfdb5bguyb62wu54nm