# Enhanced Experience Replay Generation for Efficient Reinforcement Learning

arXiv: https://arxiv.org/abs/1705.08245 · HTML: https://mlsanity.com/paper/1705.08245/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 May 2017.

## Built on by (most cited first, 6 of 6)

- [Synthetic Experience Replay](https://mlsanity.com/paper/2303.06614/) (2023)
- [Langevin Soft Actor-Critic: Efficient Exploration through Uncertainty-Driven Critic Learning](https://mlsanity.com/paper/2501.17827/) (2025)
- [Generative Models in Decision Making: A Survey](https://mlsanity.com/paper/2502.17100/) (2025)
- [Offline-to-Online Reinforcement Learning with Classifier-Free Diffusion Generation](https://mlsanity.com/paper/2508.06806/) (2025)
- [The Courage to Stop: Overcoming Sunk Cost Fallacy in Deep Reinforcement Learning](https://mlsanity.com/paper/2506.13672/) (2025)
- [Iterative Compositional Data Generation for Robot Control](https://mlsanity.com/paper/2512.10891/) (2025)

## Abstract (arXiv)

Applying deep reinforcement learning (RL) on real systems suffers from slow data sampling. We propose an enhanced generative adversarial network (EGAN) to initialize an RL agent in order to achieve faster learning. The EGAN utilizes the relation between states and actions to enhance the quality of data samples generated by a GAN. Pre-training the agent with the EGAN shows a steeper learning curve with a 20% improvement of training time in the beginning of learning, compared to no pre-training, and an improvement compared to training with GAN by about 5% with smaller variations. For real time systems with sparse and slow data sampling the EGAN could be used to speed up the early phases of the training process.