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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
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
Learning and Exploiting Multiple Subgoals for Fast Exploration in Hierarchical Reinforcement Learning—0
Using Natural Language for Reward Shaping in Reinforcement LearningCode0
Go-Explore: a New Approach for Hard-Exploration ProblemsCode1
Escape Room: A Configurable Testbed for Hierarchical Reinforcement Learning—0
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