← Back to all news
arXiv Machine Learning By Hoda Yamani, Yuning Xing, Koen van Rijnsoever, Bruce A. MacDonald, Henry Williams

Repetition as Reinforcement: Enhancing Sample Efficiency via Instant Episode Repetition in Reinforcement Learning

Read the original on arXiv Machine Learning →

The paper proposes Instant Episode Repetition (IER), a method that immediately repeats action sequences from high‑reward episodes during training to boost sample efficiency. Unlike passive replay techniques, IER actively shapes data collection by re‑executing successful behaviors for a set number of subsequent episodes. Experiments on MuJoCo, the DeepMind Control Suite, and a robotic manipulation task show that IER improves learning performance over standard SAC, TD3, and self‑imitation baselines.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

Hugging Face Trending Papers
Aug 18

Integrating Novelty and Surprise for Experience Prioritization and Exploration in Image-Based Reinforcement Learning

The paper tackles sample efficiency in image-based reinforcement learning by combining novelty and surprise signals to prioritize experiences. It proposes Novelty and Surprise Prioritized Experience Replay (NSPER) and an extended version, NSPER+R, which also uses these signals as intrinsic rewards. Experiments on DeepMind Control Suite tasks demonstrate that both methods accelerate training and improve convergence compared to existing techniques.

agentsreinforcement-learning
More like this →
arXiv AI
Aug 19

Integrating Novelty and Surprise for Experience Prioritization and Exploration in Image-Based Reinforcement Learning

The paper proposes Novelty and Surprise Prioritized Experience Replay (NSPER) for image-based reinforcement learning, combining novelty to highlight underrepresented states and surprise to reveal gaps in the agent’s knowledge. An extended version, NSPER+R, also uses these signals as intrinsic rewards to enhance both replay quality and exploration. Experiments on DeepMind Control Suite tasks demonstrate that NSPER and NSPER+R accelerate training and improve convergence compared to existing methods.

By Hoda Yamani, Henry Williams, Bruce A. MacDonald
agentsreinforcement-learning
More like this →