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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
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
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
Action-Dependent Optimality-Preserving Reward Shaping—0
GAN-based Intrinsic Exploration For Sample Efficient Reinforcement Learning—0
Generative Adversarial Exploration for Reinforcement Learning—0
Hierarchical Imitation and Reinforcement Learning—0
Sample Efficient Deep Reinforcement Learning via Local Planning—0
Int-HRL: Towards Intention-based Hierarchical Reinforcement Learning—0
Learning and Exploiting Multiple Subgoals for Fast Exploration in Hierarchical Reinforcement Learning—0
DeepSynth: Automata Synthesis for Automatic Task Segmentation in Deep Reinforcement LearningCode0
Count-Based Exploration with Neural Density ModelsCode0
Empowerment-driven Exploration using Mutual Information EstimationCode0
Combining Experience Replay with Exploration by Random Network DistillationCode0
Uncertainty - sensitive learning and planning with ensemblesCode0
Scaling All-Goals Updates in Reinforcement Learning Using Convolutional Neural NetworksCode0
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