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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 51–61 of 61 papers

TitleStatusHype
Learning High-level Representations from Demonstrations—0
Rainbow: Combining Improvements in Deep Reinforcement LearningCode3
Exploration in Feature Space for Reinforcement Learning—0
Deep Abstract Q-Networks—0
Feature Control as Intrinsic Motivation for Hierarchical Reinforcement LearningCode0
Beating Atari with Natural Language Guided Reinforcement LearningCode0
Micro-Objective Learning : Accelerating Deep Reinforcement Learning through the Discovery of Continuous Subgoals—0
Count-Based Exploration with Neural Density ModelsCode0
Playing Atari Games with Deep Reinforcement Learning and Human Checkpoint ReplayCode0
Unifying Count-Based Exploration and Intrinsic MotivationCode0
Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic MotivationCode0
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