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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 31–40 of 61 papers

TitleStatusHype
Curiosity in Hindsight: Intrinsic Exploration in Stochastic Environments—0
Deep Abstract Q-Networks—0
Memory Based Trajectory-conditioned Policies for Learning from Sparse Rewards—0
Entropic Desired Dynamics for Intrinsic Control—0
Escape Room: A Configurable Testbed for Hierarchical Reinforcement Learning—0
Exploration by Random Network Distillation—0
Exploration in Feature Space for Reinforcement Learning—0
Action-Dependent Optimality-Preserving Reward Shaping—0
GAN-based Intrinsic Exploration For Sample Efficient Reinforcement Learning—0
Generative Adversarial Exploration for Reinforcement Learning—0
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