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Found 4 papers, 4 papers with code
Exploratory Not Explanatory: Counterfactual Analysis of Saliency Maps for Deep Reinforcement Learning
Saliency maps are frequently used to support explanations of the behavior of deep reinforcement learning (RL) agents.
Toybox: A Suite of Environments for Experimental Evaluation of Deep Reinforcement Learning
We show that TOYBOX enables a wide range of experiments and analyses that are impossible in other environments.
Let's Play Again: Variability of Deep Reinforcement Learning Agents in Atari Environments
Reproducibility in reinforcement learning is challenging: uncontrolled stochasticity from many sources, such as the learning algorithm, the learned policy, and the environment itself have led researchers to report the performance of learned agents using aggregate metrics of performance over multiple random seeds for a single environment.
ToyBox: Better Atari Environments for Testing Reinforcement Learning Agents
It is a widely accepted principle that software without tests has bugs.
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