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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
Action-Dependent Optimality-Preserving Reward Shaping—0
GAN-based Intrinsic Exploration For Sample Efficient Reinforcement Learning—0
Generative Adversarial Exploration for Reinforcement Learning—0
Hierarchical Imitation and Reinforcement Learning—0
Sample Efficient Deep Reinforcement Learning via Local Planning—0
Int-HRL: Towards Intention-based Hierarchical Reinforcement Learning—0
Learning and Exploiting Multiple Subgoals for Fast Exploration in Hierarchical Reinforcement Learning—0
Deep Curiosity Search: Intra-Life Exploration Can Improve Performance on Challenging Deep Reinforcement Learning Problems—0
Learning Montezuma's Revenge from a Single Demonstration—0
Learning Representations in Model-Free Hierarchical Reinforcement Learning—0
Micro-Objective Learning : Accelerating Deep Reinforcement Learning through the Discovery of Continuous Subgoals—0
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