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Montezuma's Revenge

Montezuma's Revenge is an ATARI 2600 Benchmark game that is known to be difficult to perform on for reinforcement learning algorithms. Solutions typically employ algorithms that incentivise environment exploration in different ways.

For the state-of-the art tables, please consult the parent Atari Games task.

( Image credit: Q-map )

Papers

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Showing 31–40 of 61 papers

TitleStatusHype
Learning Representations in Model-Free Hierarchical Reinforcement Learning—0
Micro-Objective Learning : Accelerating Deep Reinforcement Learning through the Discovery of Continuous Subgoals—0
MIME: Mutual Information Minimisation Exploration—0
Observe and Look Further: Achieving Consistent Performance on Atari—0
On Bonus Based Exploration Methods In The Arcade Learning Environment—0
On Bonus-Based Exploration Methods in the Arcade Learning Environment—0
Parametrically Retargetable Decision-Makers Tend To Seek Power—0
Paused Agent Replay Refresh—0
Benchmarking Bonus-Based Exploration Methods on the Arcade Learning Environment—0
Bridging the Gap: Providing Post-Hoc Symbolic Explanations for Sequential Decision-Making Problems with Inscrutable Representations—0
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