SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 26–50 of 61 papers

TitleStatusHype
Combining Experience Replay with Exploration by Random Network DistillationCode0
Using Natural Language for Reward Shaping in Reinforcement LearningCode0
Count-Based Exploration with Neural Density ModelsCode0
Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic MotivationCode0
Uncertainty - sensitive learning and planning with ensemblesCode0
Exploring Unknown States with Action BalanceCode0
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
Exploration in Feature Space for Reinforcement Learning—0
Show:102550
← PrevPage 2 of 3Next →

No leaderboard results yet.