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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
Paused Agent Replay Refresh—0
GAN-based Intrinsic Exploration For Sample Efficient Reinforcement Learning—0
Parametrically Retargetable Decision-Makers Tend To Seek Power—0
Understanding and Preventing Capacity Loss in Reinforcement Learning—0
Generative Adversarial Exploration for Reinforcement Learning—0
Exploration by Random Network Distillation—0
Creativity of AI: Hierarchical Planning Model Learning for Facilitating Deep Reinforcement Learning—0
Entropic Desired Dynamics for Intrinsic Control—0
On Bonus-Based Exploration Methods in the Arcade Learning Environment—0
Learning Abstract Models for Strategic Exploration and Fast Reward TransferCode0
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