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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 51–60 of 61 papers

Title	Date	Tasks	Status
Learning Abstract Models for Strategic Exploration and Fast Reward Transfer	Jul 12, 2020	Atari GamesModel-based Reinforcement Learning	CodeCode Available
Playing Atari Games with Deep Reinforcement Learning and Human Checkpoint Replay	Jul 18, 2016	Atari GamesDeep Reinforcement Learning	CodeCode Available
Exploring Unknown States with Action Balance	Mar 10, 2020	Atari GamesDeep Reinforcement Learning	CodeCode Available
Feature Control as Intrinsic Motivation for Hierarchical Reinforcement Learning	May 18, 2017	Hierarchical Reinforcement LearningMontezuma's Revenge	CodeCode Available
Fine-tuning Reinforcement Learning Models is Secretly a Forgetting Mitigation Problem	Feb 5, 2024	Montezuma's RevengeNetHack	CodeCode Available
A Study of Plasticity Loss in On-Policy Deep Reinforcement Learning	May 29, 2024	Continual LearningDeep Reinforcement Learning	CodeCode Available
Unifying Count-Based Exploration and Intrinsic Motivation	Jun 6, 2016	Atari GamesMontezuma's Revenge	CodeCode Available
Beating Atari with Natural Language Guided Reinforcement Learning	Apr 18, 2017	Atari GamesDeep Reinforcement Learning	CodeCode Available
Uncertainty-sensitive Learning and Planning with Ensembles	Dec 19, 2019	Montezuma's RevengeReinforcement Learning	CodeCode Available
Using Natural Language for Reward Shaping in Reinforcement Learning	Mar 5, 2019	Atari GamesMontezuma's Revenge	CodeCode Available

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