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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

Showing 110 of 61 papers

TitleStatusHype
Rainbow: Combining Improvements in Deep Reinforcement LearningCode3
PoE-World: Compositional World Modeling with Products of Programmatic ExpertsCode1
A Study of Global and Episodic Bonuses for Exploration in Contextual MDPsCode1
Flipping Coins to Estimate Pseudocounts for Exploration in Reinforcement LearningCode1
Redeeming Intrinsic Rewards via Constrained OptimizationCode1
Hybrid RL: Using Both Offline and Online Data Can Make RL EfficientCode1
Cell-Free Latent Go-ExploreCode1
Open-Ended Reinforcement Learning with Neural Reward FunctionsCode1
NovelD: A Simple yet Effective Exploration CriterionCode1
Reinforcement Learning with Latent FlowCode1
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