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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 1–25 of 61 papers

TitleStatusHype
Rainbow: Combining Improvements in Deep Reinforcement LearningCode3
PoE-World: Compositional World Modeling with Products of Programmatic ExpertsCode1
Flipping Coins to Estimate Pseudocounts for Exploration in Reinforcement LearningCode1
A Study of Global and Episodic Bonuses for Exploration in Contextual MDPsCode1
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
First return, then exploreCode1
Go-Explore: a New Approach for Hard-Exploration ProblemsCode1
Exploration by Random Network DistillationCode1
Playing hard exploration games by watching YouTubeCode1
Action-Dependent Optimality-Preserving Reward Shaping—0
A Study of Plasticity Loss in On-Policy Deep Reinforcement LearningCode0
Fine-tuning Reinforcement Learning Models is Secretly a Forgetting Mitigation ProblemCode0
Int-HRL: Towards Intention-based Hierarchical Reinforcement Learning—0
Sample Efficient Deep Reinforcement Learning via Local Planning—0
Curiosity in Hindsight: Intrinsic Exploration in Stochastic Environments—0
Paused Agent Replay Refresh—0
GAN-based Intrinsic Exploration For Sample Efficient Reinforcement Learning—0
Parametrically Retargetable Decision-Makers Tend To Seek Power—0
Understanding and Preventing Capacity Loss in Reinforcement Learning—0
Generative Adversarial Exploration for Reinforcement Learning—0
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