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An Atari Model Zoo for Analyzing, Visualizing, and Comparing Deep Reinforcement Learning Agents

2018-12-17 · Felipe Petroski Such, Vashisht Madhavan, Rosanne Liu, Rui Wang, Pablo Samuel Castro, Yulun Li, Jiale Zhi, Ludwig Schubert, Marc G. Bellemare, Jeff Clune, Joel Lehman

Much human and computational effort has aimed to improve how deep reinforcement learning algorithms perform on benchmarks such as the Atari Learning Environment. Comparatively less effort has focused on understanding what has been learned by such methods, and investigating and comparing the representations learned by different families of reinforcement learning (RL) algorithms. Sources of friction include the onerous computational requirements, and general logistical and architectural complications for running Deep RL algorithms at scale. We lessen this friction, by (1) training several algorithms at scale and releasing trained models, (2) integrating with a previous Deep RL model release, and (3) releasing code that makes it easy for anyone to load, visualize, and analyze such models. This paper introduces the Atari Zoo framework, which contains models trained across benchmark Atari games, in an easy-to-use format, as well as code that implements common modes of analysis and connects such models to a popular neural network visualization library. Further, to demonstrate the potential of this dataset and software package, we show initial quantitative and qualitative comparisons between the performance and representations of several deep RL algorithms, highlighting interesting and previously unknown distinctions between them.

📄 PDF Abstract BibTeX arXiv:1812.07069

Code (1)

uber-research/atari-model-zoo 공식 구현 tf

Tasks

Atari GamesDeep Reinforcement LearningFrictionreinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Visual Rationalizations in Deep Reinforcement Learning for Atari Games

2019-02-01 · Laurens Weitkamp, Elise van der Pol, Zeynep Akata

Due to the capability of deep learning to perform well in high dimensional problems, deep reinforcement learning agents perform well in challenging tasks such as Atari 2600 games. However, clearly explaining why a certai…

Atari Games Decision Making Deep Learning Deep Reinforcement Learning+3

Visualizing and Understanding Atari Agents

2017-10-31 · ICML 2018 7 · Sam Greydanus, Anurag Koul, Jonathan Dodge, Alan Fern

While deep reinforcement learning (deep RL) agents are effective at maximizing rewards, it is often unclear what strategies they use to do so. In this paper, we take a step toward explaining deep RL agents through a case…

Deep Reinforcement Learning Reinforcement Learning Reinforcement Learning (RL)

Learn to Interpret Atari Agents

2018-12-29 · Zhao Yang, Song Bai, Li Zhang, Philip H. S. Torr

Deep reinforcement learning (DeepRL) agents surpass human-level performance in many tasks. However, the direct mapping from states to actions makes it hard to interpret the rationale behind the decision-making of the age…

Decision Making Deep Reinforcement Learning Reinforcement Learning

Virtual Augmented Reality for Atari Reinforcement Learning

2023-10-12 · Christian A. Schiller

Reinforcement Learning (RL) has achieved significant milestones in the gaming domain, most notably Google DeepMind's AlphaGo defeating human Go champion Ken Jie. This victory was also made possible through the Atari Lear…

Image Segmentation reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)+1

Machine versus Human Attention in Deep Reinforcement Learning Tasks

2020-10-29 · NeurIPS 2021 12 · Sihang Guo, Ruohan Zhang, Bo Liu, Yifeng Zhu 외

Deep reinforcement learning (RL) algorithms are powerful tools for solving visuomotor decision tasks. However, the trained models are often difficult to interpret, because they are represented as end-to-end deep neural n…

Atari Games Deep Reinforcement Learning reinforcement-learning Reinforcement Learning+1