Reproducibility

Benchmarking and Evaluation in Inverse Reinforcement Learning

Benchmarks are particularly useful for characterizing algorithms and determining their usefulness in different settings. Here I highlight the need for more standardization of performance evaluations in inverse reinforcement learning.

Reproducibility and Replicability in Deep Reinforcement Learning (and Other Deep Learning Methods)

In recent years, significant progress has been made in solving challenging problems using deep learning. Reproducing existing work and accurately judging the improvements offered by novel methods is vital to sustaining this progress. Unfortunately, …

Show Me the Data! On the Reproducibility of Policy Gradient Methods for Continuous Control

A brief discussion on some difficulties that students may encounter in reproducing modern policy gradient methods in continuous control tasks and best practices for writing papers on these methods.