SODACER uses fast and slow buffers with adaptive clustering for experience replay in safe RL, integrated with CBFs and Sophia optimizer to achieve faster convergence and safety on nonlinear systems like HPV transmission.
A Deeper Look at Experience Replay
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
abstract
Recently experience replay is widely used in various deep reinforcement learning (RL) algorithms, in this paper we rethink the utility of experience replay. It introduces a new hyper-parameter, the memory buffer size, which needs carefully tuning. However unfortunately the importance of this new hyper-parameter has been underestimated in the community for a long time. In this paper we did a systematic empirical study of experience replay under various function representations. We showcase that a large replay buffer can significantly hurt the performance. Moreover, we propose a simple O(1) method to remedy the negative influence of a large replay buffer. We showcase its utility in both simple grid world and challenging domains like Atari games.
years
2026 2verdicts
UNVERDICTED 2representative citing papers
AGMARL-DKS uses per-node multi-agent RL with GNN state representations and stress-aware lexicographical ordering to outperform the default Kubernetes scheduler on fault tolerance, utilization, and cost for batch and mission-critical workloads.
citing papers explorer
-
Self-Organizing Dual-Buffer Adaptive Clustering Experience Replay (SODACER) for Safe Reinforcement Learning in Optimal Control
SODACER uses fast and slow buffers with adaptive clustering for experience replay in safe RL, integrated with CBFs and Sophia optimizer to achieve faster convergence and safety on nonlinear systems like HPV transmission.
-
AGMARL-DKS: An Adaptive Graph-Enhanced Multi-Agent Reinforcement Learning for Dynamic Kubernetes Scheduling
AGMARL-DKS uses per-node multi-agent RL with GNN state representations and stress-aware lexicographical ordering to outperform the default Kubernetes scheduler on fault tolerance, utilization, and cost for batch and mission-critical workloads.