Empirical Studies
Summary
Empirical studies play a crucial role in advancing our understanding of reinforcement learning (RL) algorithms and their practical implementation. One significant study in this area focused on on-policy RL for continuous control tasks, examining over 50 different design choices that impact agent performance. By implementing these choices in a unified framework and training over 250,000 agents across five environments of varying complexity, the researchers were able to provide valuable insights and practical recommendations for on-policy RL training. This large-scale empirical approach helps bridge the gap between theoretical descriptions of algorithms and their real-world implementations, addressing the issue of discrepancies that can hinder progress in the field. Such studies are essential for attributing progress in RL accurately and accelerating overall advancements in the domain.