Atari Games

65개 벤치마크 · 논문 658편 · 이 태스크의 논문 보기 →

Benchmarks

Atari 2600 Montezuma's Revenge

결과 100개

Most implemented

Playing Atari with Deep Reinforcement Learning

2013-12-19 · 구현 112개

Deep Reinforcement Learning with Double Q-learning

2015-09-22 · 구현 97개

Dueling Network Architectures for Deep Reinforcement Learning

2015-11-20 · 구현 73개

Asynchronous Methods for Deep Reinforcement Learning

2016-02-04 · 구현 70개

Rainbow: Combining Improvements in Deep Reinforcement Learning

2017-10-06 · 구현 34개

Papers

Q-based Variational Inverse Reinforcement Learning

2026-08-17 · Ondrej Bajgar, Peter Tisnikar, Alessandro Abate, Konstantinos Gatsis 외 arxiv

The development of safe and beneficial AI requires that systems can learn and act in accordance with human preferences. However, explicitly specifying these preferences by hand is often infeasible. Inverse reinforcement …

Reinforcement Learning Active Learning Atari Games

Accelerating Q-learning through Efficient Value-Sharing across Actions

2026-06-29 · Prabhat Nagarajan, Brett Daley, Martha White, Marlos C. Machado arxiv

Action values are foundational to many control algorithms such as Q-learning. Therefore, efficient action-value learning is central to reinforcement learning (RL). However, learning them can be slow, requiring many updat…

Reinforcement Learning Atari Games

The Latent Bridge: A Continuous Slow-Fast Channel for Real-Time Game Agents

2026-06-23 · Bojie Li, Noah Shi arxiv

A real-time agent for general computer use - with games as the most demanding case - must act within tens of milliseconds while still planning over seconds. These two regimes sit at opposite ends of the latency-quality t…

Atari Games

Multivariate Distributional Reinforcement Learning Using Sliced Divergences

2026-05-29 · Baptiste Debes, Tinne Tuytelaars arxiv

Distributional reinforcement learning (DRL) models the full return distribution rather than expectations, but extending it to multivariate settings remains challenging. Many common metrics do not naturally generalize bey…

Reinforcement Learning Atari Games

Plan2Cleanse: Test-Time Backdoor Defense via Monte-Carlo Planning in Deep Reinforcement Learning

2026-05-10 · Sze-Ann Chen, Zhi-Yi Chin, Kui-Yuan Chen, Chi-Yu Li 외 arxiv

Ensuring the security of reinforcement learning (RL) models is critical, particularly when they are trained by third parties and deployed in real-world systems. Attackers can implant backdoors into these models, causing …

Reinforcement Learning Atari Games

Revisiting Adam for Streaming Reinforcement Learning

2026-05-07 · Florin Gogianu, Adrian Catalin Lutu, Razvan Pascanu arxiv

Learning from a sequence of interactions, as soon as observations are perceived and acted upon, without explicitly storing them, holds the promise of simpler, more efficient and adaptive algorithms. For over a decade, ho…

Reinforcement Learning Atari Games

전체 658편 보기 →