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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 41–50 of 61 papers

TitleStatusHype
Learning and Exploiting Multiple Subgoals for Fast Exploration in Hierarchical Reinforcement Learning—0
Using Natural Language for Reward Shaping in Reinforcement LearningCode0
Escape Room: A Configurable Testbed for Hierarchical Reinforcement Learning—0
Learning Montezuma's Revenge from a Single Demonstration—0
Contingency-Aware Exploration in Reinforcement Learning—0
Learning Representations in Model-Free Hierarchical Reinforcement Learning—0
Empowerment-driven Exploration using Mutual Information EstimationCode0
Scaling All-Goals Updates in Reinforcement Learning Using Convolutional Neural NetworksCode0
Deep Curiosity Search: Intra-Life Exploration Can Improve Performance on Challenging Deep Reinforcement Learning Problems—0
Observe and Look Further: Achieving Consistent Performance on Atari—0
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