We’ve found that adding adaptive noise to the parameters of reinforcement learning algorithms frequently boosts performance. This exploration method is simple to implement and very rarely decreases performance, so it’s worth trying on any problem.
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arXiv:2607. 29419v1 Announce Type: cross Abstract: In reinforcement learning, exploration with sparse and delayed rewards presents a significant challenge due to the limited feedback available for guiding the learning process.
arXiv:2606. 16590v1 Announce Type: cross Abstract: Exploration in deep reinforcement learning (RL) is commonly implemented as temporally uncorrelated white noise.
arXiv:2608.21995v1 Announce Type: cross Abstract: We propose Variance Driven Exploration (VarDE), a principled approach for pure exploration in highly stochastic environments, where the exploration p...
The paper introduces Gradient‑Momentum Coupling (GMC), a method that quantifies learning progress by measuring how strongly a sample influences changes in the parameter space, using the normalized absolute product of its gradient and the momentum of previous gradients. GMC filters out noise by accumulating consistent directions of change while canceling random fluctuations, leading to a more uniform prioritization across tasks with varying noise levels and better ranking of learnable tasks by improvement speed. Experiments on MiniGrid MultiRoom tasks show that replacing prediction error with GMC in the Intrinsic Curiosity Module restores exploration capabilities that were lost to unpredictable observations.
arXiv:2608. 15700v1 Announce Type: new Abstract: Background: Distillation of training targets generated thru search/planning has proven useful in reinforcement learning, but search can take exceedingly long.
arXiv:2606. 05888v1 Announce Type: new Abstract: Retry-based objectives such as pass@K and max@K optimize the best return obtained from multiple sampled trajectories, and recent work has shown that they can promote exploration without explicit exploration bonuses.