Montezuma's Revenge
28 papers with code • 1 benchmarks • 1 datasets
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 )
Benchmarks
These leaderboards are used to track progress in Montezuma's Revenge
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Arcade Learning Environment
Latest papers
A Study of Global and Episodic Bonuses for Exploration in Contextual MDPs
This results in an algorithm which sets a new state of the art across 16 tasks from the MiniHack suite used in prior work, and also performs robustly on Habitat and Montezuma's Revenge.
Flipping Coins to Estimate Pseudocounts for Exploration in Reinforcement Learning
samlobel/cfn
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We propose a new method for count-based exploration in high-dimensional state spaces.
Redeeming Intrinsic Rewards via Constrained Optimization
However, on easy exploration tasks, the agent gets distracted by intrinsic rewards and performs unnecessary exploration even when sufficient task (also called extrinsic) reward is available.
Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient
yudasong/hyq
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We consider a hybrid reinforcement learning setting (Hybrid RL), in which an agent has access to an offline dataset and the ability to collect experience via real-world online interaction.
Cell-Free Latent Go-Explore
qgallouedec/lge
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In this paper, we introduce Latent Go-Explore (LGE), a simple and general approach based on the Go-Explore paradigm for exploration in reinforcement learning (RL).
Open-Ended Reinforcement Learning with Neural Reward Functions
amujika/open-ended-reinforcement-learning-with-neural-reward-functions
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Inspired by the great success of unsupervised learning in Computer Vision and Natural Language Processing, the Reinforcement Learning community has recently started to focus more on unsupervised discovery of skills.
NovelD: A Simple yet Effective Exploration Criterion
tianjunz/NovelD
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NeurIPS 2021
We analyze NovelD thoroughly in MiniGrid and found that empirically it helps the agent explore the environment more uniformly with a focus on exploring beyond the boundary.
Reinforcement Learning with Latent Flow
WendyShang/flare
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NeurIPS 2021
Temporal information is essential to learning effective policies with Reinforcement Learning (RL).
Learning Abstract Models for Strategic Exploration and Fast Reward Transfer
google-research/google-research
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Model-based reinforcement learning (RL) is appealing because (i) it enables planning and thus more strategic exploration, and (ii) by decoupling dynamics from rewards, it enables fast transfer to new reward functions.
First return, then explore
The promise of reinforcement learning is to solve complex sequential decision problems autonomously by specifying a high-level reward function only.
