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

by   Felipe Petroski Such, et al.

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.


page 6

page 8

page 12

page 16

page 17

page 18

page 20

page 21


Human versus Machine Attention in Deep Reinforcement Learning Tasks

Deep reinforcement learning (RL) algorithms are powerful tools for solvi...

rlpyt: A Research Code Base for Deep Reinforcement Learning in PyTorch

Since the recent advent of deep reinforcement learning for game play and...

FlashRL: A Reinforcement Learning Platform for Flash Games

Reinforcement Learning (RL) is a research area that has blossomed tremen...

Challenges of Context and Time in Reinforcement Learning: Introducing Space Fortress as a Benchmark

Research in deep reinforcement learning (RL) has coalesced around improv...

Prevalence of Code Smells in Reinforcement Learning Projects

Reinforcement Learning (RL) is being increasingly used to learn and adap...

Behaviour Suite for Reinforcement Learning

This paper introduces the Behaviour Suite for Reinforcement Learning, or...

TD or not TD: Analyzing the Role of Temporal Differencing in Deep Reinforcement Learning

Our understanding of reinforcement learning (RL) has been shaped by theo...

Please sign up or login with your details

Forgot password? Click here to reset