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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

TitleStatusHype
Learning Representations in Model-Free Hierarchical Reinforcement Learning—0
Exploration in Feature Space for Reinforcement Learning—0
Exploration by Random Network Distillation—0
Creativity of AI: Hierarchical Planning Model Learning for Facilitating Deep Reinforcement Learning—0
Curiosity in Hindsight: Intrinsic Exploration in Stochastic Environments—0
Action-Dependent Optimality-Preserving Reward Shaping—0
Escape Room: A Configurable Testbed for Hierarchical Reinforcement Learning—0
GAN-based Intrinsic Exploration For Sample Efficient Reinforcement Learning—0
Generative Adversarial Exploration for Reinforcement Learning—0
Contingency-Aware Exploration in Reinforcement Learning—0
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