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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 125 of 61 papers

TitleStatusHype
Rainbow: Combining Improvements in Deep Reinforcement LearningCode3
Cell-Free Latent Go-ExploreCode1
Hybrid RL: Using Both Offline and Online Data Can Make RL EfficientCode1
Go-Explore: a New Approach for Hard-Exploration ProblemsCode1
PoE-World: Compositional World Modeling with Products of Programmatic ExpertsCode1
First return, then exploreCode1
Reinforcement Learning with Latent FlowCode1
Redeeming Intrinsic Rewards via Constrained OptimizationCode1
A Study of Global and Episodic Bonuses for Exploration in Contextual MDPsCode1
Flipping Coins to Estimate Pseudocounts for Exploration in Reinforcement LearningCode1
NovelD: A Simple yet Effective Exploration CriterionCode1
Exploration by Random Network DistillationCode1
Open-Ended Reinforcement Learning with Neural Reward FunctionsCode1
Playing hard exploration games by watching YouTubeCode1
Playing Atari Games with Deep Reinforcement Learning and Human Checkpoint ReplayCode0
Learning Abstract Models for Strategic Exploration and Fast Reward TransferCode0
Feature Control as Intrinsic Motivation for Hierarchical Reinforcement LearningCode0
Exploring Unknown States with Action BalanceCode0
Scaling All-Goals Updates in Reinforcement Learning Using Convolutional Neural NetworksCode0
Count-Based Exploration with Neural Density ModelsCode0
Beating Atari with Natural Language Guided Reinforcement LearningCode0
A Study of Plasticity Loss in On-Policy Deep Reinforcement LearningCode0
Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic MotivationCode0
Empowerment-driven Exploration using Mutual Information EstimationCode0
Fine-tuning Reinforcement Learning Models is Secretly a Forgetting Mitigation ProblemCode0
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