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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 21–30 of 61 papers

Title	Date	Tasks	Status	Score
Count-Based Exploration with Neural Density Models	Mar 3, 2017	Atari GamesMontezuma's Revenge	CodeCode Available	5
Learning Abstract Models for Strategic Exploration and Fast Reward Transfer	Jul 12, 2020	Atari GamesModel-based Reinforcement Learning	CodeCode Available	5
Beating Atari with Natural Language Guided Reinforcement Learning	Apr 18, 2017	Atari GamesDeep Reinforcement Learning	CodeCode Available	5
A Study of Plasticity Loss in On-Policy Deep Reinforcement Learning	May 29, 2024	Continual LearningDeep Reinforcement Learning	CodeCode Available	5
Fine-tuning Reinforcement Learning Models is Secretly a Forgetting Mitigation Problem	Feb 5, 2024	Montezuma's RevengeNetHack	CodeCode Available	5
Empowerment-driven Exploration using Mutual Information Estimation	Oct 11, 2018	Deep Reinforcement LearningMontezuma's Revenge	CodeCode Available	5
DeepSynth: Automata Synthesis for Automatic Task Segmentation in Deep Reinforcement Learning	Nov 22, 2019	Deep Reinforcement LearningHierarchical Reinforcement Learning	CodeCode Available	5
Combining Experience Replay with Exploration by Random Network Distillation	May 18, 2019	Atari GamesMontezuma's Revenge	CodeCode Available	5
Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation	Apr 20, 2016	Deep Reinforcement LearningMontezuma's Revenge	CodeCode Available	5
Unifying Count-Based Exploration and Intrinsic Motivation	Jun 6, 2016	Atari GamesMontezuma's Revenge	CodeCode Available	5

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