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 11–20 of 61 papers

TitleStatusHype
Redeeming Intrinsic Rewards via Constrained OptimizationCode1
Reinforcement Learning with Latent FlowCode1
Go-Explore: a New Approach for Hard-Exploration ProblemsCode1
PoE-World: Compositional World Modeling with Products of Programmatic ExpertsCode1
Deep Curiosity Search: Intra-Life Exploration Can Improve Performance on Challenging Deep Reinforcement Learning Problems—0
Deep Abstract Q-Networks—0
Bridging the Gap: Providing Post-Hoc Symbolic Explanations for Sequential Decision-Making Problems with Inscrutable Representations—0
Curiosity in Hindsight: Intrinsic Exploration in Stochastic Environments—0
Creativity of AI: Hierarchical Planning Model Learning for Facilitating Deep Reinforcement Learning—0
Benchmarking Bonus-Based Exploration Methods on the Arcade Learning Environment—0
Show:102550
← PrevPage 2 of 7Next →

No leaderboard results yet.