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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
Exploring Unknown States with Action BalanceCode0
Bridging the Gap: Providing Post-Hoc Symbolic Explanations for Sequential Decision-Making Problems with Inscrutable Representations—0
MIME: Mutual Information Minimisation Exploration—0
On Bonus Based Exploration Methods In The Arcade Learning Environment—0
Uncertainty-sensitive Learning and Planning with EnsemblesCode0
DeepSynth: Automata Synthesis for Automatic Task Segmentation in Deep Reinforcement LearningCode0
Uncertainty - sensitive learning and planning with ensemblesCode0
Benchmarking Bonus-Based Exploration Methods on the Arcade Learning Environment—0
Memory Based Trajectory-conditioned Policies for Learning from Sparse Rewards—0
Combining Experience Replay with Exploration by Random Network DistillationCode0
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