Loading Now

Summary of Cuer: Corrected Uniform Experience Replay For Off-policy Continuous Deep Reinforcement Learning Algorithms, by Arda Sarp Yenicesu et al.


CUER: Corrected Uniform Experience Replay for Off-Policy Continuous Deep Reinforcement Learning Algorithms

by Arda Sarp Yenicesu, Furkan B. Mutlu, Suleyman S. Kozat, Ozgur S. Oguz

First submitted to arxiv on: 13 Jun 2024

Categories

  • Main: Machine Learning (cs.LG)
  • Secondary: Artificial Intelligence (cs.AI)

     Abstract of paper      PDF of paper


GrooveSquid.com Paper Summaries

GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!

Summary difficulty Written by Summary
High Paper authors High Difficulty Summary
Read the original abstract here
Medium GrooveSquid.com (original content) Medium Difficulty Summary
The utilization of experience replay prioritization algorithms can enhance computing efficiency by reassessing the importance of transitions as they are sampled. However, these algorithms often ignore the dynamic nature of transition importance, which can negatively impact agent performance. This paper presents a novel algorithm, Corrected Uniform Experience Replay (CUER), that addresses this issue by stochastically sampling stored experiences while considering fairness among all other experiences. CUER shows promising improvements for off-policy continuous control algorithms in terms of sample efficiency, final performance, and stability of the policy during training.
Low GrooveSquid.com (original content) Low Difficulty Summary
Experience replay helps agents learn from past experiences. This technique can be improved by prioritizing important transitions as they are sampled. However, this approach can have negative effects if it doesn’t consider how the agent’s policy changes over time. A new algorithm called CUER is introduced to solve this problem. It makes sure that all stored experiences are considered fairly and equally, which leads to better performance for off-policy continuous control algorithms.

Keywords

» Artificial intelligence