Context tree maximizing reinforcement learning
Nguyen, Phuong; Sunehag, Peter; Hutter, Marcus
Recent developments in reinforcement learning for non-Markovian problems witness a surge in history-based methods, among which we are particularly interested in two frameworks, ΦMDP and MC-AIXI-CTW. ΦMDP attempts to reduce the general RL problem, where
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|Source:||Proceedings of the National Conference on Artificial Intelligence|
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