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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 26–50 of 61 papers

TitleStatusHype
Exploring Unknown States with Action BalanceCode0
Count-Based Exploration with Neural Density ModelsCode0
Uncertainty - sensitive learning and planning with ensemblesCode0
Feature Control as Intrinsic Motivation for Hierarchical Reinforcement LearningCode0
Fine-tuning Reinforcement Learning Models is Secretly a Forgetting Mitigation ProblemCode0
Learning Representations in Model-Free Hierarchical Reinforcement Learning—0
Micro-Objective Learning : Accelerating Deep Reinforcement Learning through the Discovery of Continuous Subgoals—0
MIME: Mutual Information Minimisation Exploration—0
Observe and Look Further: Achieving Consistent Performance on Atari—0
On Bonus Based Exploration Methods In The Arcade Learning Environment—0
On Bonus-Based Exploration Methods in the Arcade Learning Environment—0
Parametrically Retargetable Decision-Makers Tend To Seek Power—0
Paused Agent Replay Refresh—0
Benchmarking Bonus-Based Exploration Methods on the Arcade Learning Environment—0
Bridging the Gap: Providing Post-Hoc Symbolic Explanations for Sequential Decision-Making Problems with Inscrutable Representations—0
Understanding and Preventing Capacity Loss in Reinforcement Learning—0
Contingency-Aware Exploration in Reinforcement Learning—0
Creativity of AI: Hierarchical Planning Model Learning for Facilitating Deep Reinforcement Learning—0
Curiosity in Hindsight: Intrinsic Exploration in Stochastic Environments—0
Deep Abstract Q-Networks—0
Learning High-level Representations from Demonstrations—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
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