mlsanityData to 14 Oct 2026

Attention Loss Adjusted Prioritized Experience Replay

arXiv 2309.06684 ↗First posted 13 Sep 2023cs.LG cs.AIcited by 1

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Abstract (arXiv)

Prioritized Experience Replay (PER) is a technical means of deep reinforcement learning by selecting experience samples with more knowledge quantity to improve the training rate of neural network. However, the non-uniform sampling used in PER inevitably shifts the state-action space distribution and brings the estimation error of Q-value function. In this paper, an Attention Loss Adjusted Prioritized (ALAP) Experience Replay algorithm is proposed, which integrates the improved Self-Attention network with Double-Sampling mechanism to fit the hyperparameter that can regulate the importance sampling weights to eliminate the estimation error caused by PER. In order to verify the effectiveness and generality of the algorithm, the ALAP is tested with value-function based, policy-gradient based and multi-agent reinforcement learning algorithms in OPENAI gym, and comparison studies verify the advantage and efficiency of the proposed training framework.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 15 of its 33 references that are in the map. First posted on arXiv 13 Sep 2023.

Builds on

15 of 33 references.

References · 15

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015Continuous control with deep reinforcement learningcited by 3,407
  3. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  4. 2016OpenAI Gymcited by 1,736
  5. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,558
  6. 2015Prioritized Experience Replaycited by 1,099
  7. 2017Rainbow: Combining Improvements in Deep Reinforcement Learningcited by 786
  8. 2018Actor-Attention-Critic for Multi-Agent Reinforcement Learningcited by 261
  9. 2020An Equivalence between Loss Functions and Non-Uniform Sampling in Experience Replaycited by 41
  10. 2021Generalized Huber Loss for Robust Learning and its Efficient Minimization for a Robust Statisticscited by 32
  11. 2017The Effects of Memory Replay in Reinforcement Learningcited by 23
  12. 2022Actor Prioritized Experience Replaycited by 20
3 more
  1. 2013Planning by Prioritized Sweeping with Small Backupscited by 6
  2. 2019Deep Q-Learning with Q-Matrix Transfer Learning for Novel Fire Evacuation Environmentcited by 6
  3. 2021Explanation-Aware Experience Replay in Rule-Dense Environmentscited by 2

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Cited by year

Citing papers per year: 2023: 1.

1 paper.

  1. 2023Directly Attention Loss Adjusted Prioritized Experience Replaycited by 1