arXiv AI By Akhil Bagaria, Anita De Mello Koch, George Konidaris

Going Beyond State-Reaching: Learning Abstractions for Intrinsically Motivated Option Discovery

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arXiv AI
Sep 3

Action abstractions for amortized sampling

The paper introduces a method that integrates action abstraction into policy optimization for reinforcement learning and generative flow networks. By iteratively identifying frequently used action subsequences in high‑reward trajectories and treating them as single high‑level actions, the approach expands the action space and improves sample efficiency. Experiments on synthetic and real‑world tasks show that this technique discovers diverse high‑reward states more effectively, especially on challenging exploration problems, and yields interpretable abstract actions that reflect the underlying reward structure.

By Oussama Boussif, L\'ena N\'ehale Ezzine, Joseph D Viviano, Micha{\l} Koziarski, Moksh Jain, Esmeralda S. Whitammer, Emmanuel Bengio, Rim Assouel, Yoshua Bengio
arXiv AI
Sep 10

Efficient Exploration Is Enough

arXiv:2609.07575v1 Announce Type: cross Abstract: This work introduces an alternative view of efficient exploration and studies its theoretical and empirical implications in the absence of extrinsic...

By Mikel Malag\'on, Jon Vadillo, Josu Ceberio, Michael Bowling, Jose A. Lozano
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
arXiv Machine Learning
Sep 11

From Connectivity to Rewards: Dense Reward Learning with Directed State Graphs

The paper introduces Graph-Guided Quasimetric Dense Reward (G2QDR), a framework that learns a state connectivity model to predict pairwise connectivity strengths in asymmetric environments. These strengths are converted into scalar auxiliary dense rewards, offering continuous guidance across hierarchical levels. G2QDR can be integrated into any existing Goal-Conditioned Hierarchical Reinforcement Learning architecture and shows empirical performance improvements in sparse reward settings with modest computational cost.

By Shuyuan Zhang, Zihan Wang, Xiao-Wen Chang, Doina Precup