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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 1–10 of 61 papers

TitleStatusHype
Action-Dependent Optimality-Preserving Reward Shaping—0
PoE-World: Compositional World Modeling with Products of Programmatic ExpertsCode1
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
A Study of Global and Episodic Bonuses for Exploration in Contextual MDPsCode1
Flipping Coins to Estimate Pseudocounts for Exploration in Reinforcement LearningCode1
Sample Efficient Deep Reinforcement Learning via Local Planning—0
Curiosity in Hindsight: Intrinsic Exploration in Stochastic Environments—0
Redeeming Intrinsic Rewards via Constrained OptimizationCode1
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