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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 11–20 of 61 papers

TitleStatusHype
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
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