Rishabh Agarwal

Rishabh Agarwal, a research scientist at Google Brain in Montreal, dives into his award-winning paper on deep reinforcement learning. The discussion reveals how traditional performance evaluations can lead to misleading conclusions due to random seed variability. Rishabh highlights the challenges of current benchmarking methods, advocating for better reporting practices. With insights on the importance of uncertainty in results, he calls for a shift in academic standards to improve research integrity. Open-source tools aim to enhance evaluation methods, fostering greater transparency in the field.