Importance of using appropriate baselines for evaluation of data-efficiency in deep reinforcement learning for Atari

Understand

Reinforcement learning (RL) has seen great advancements in the past few years.

• Nevertheless, the consensus among the RL community is that currently used methods, despite all their benefits, suffer from extreme data inefficiency, especially in the rich visual domains like Atari.
• To circumvent this problem, novel approaches were introduced that often claim to be much more efficient than popular variations of the state-of-the-art DQN algorithm.
• In this paper, however, we demonstrate that the newly proposed techniques simply used unfair baselines in their experiments.