arXiv:2605.31289v1 Announce Type: new Abstract: Representation learning is a powerful tool for spatio-temporal abstraction within reinforcement learning (RL). Two well established approaches are through the successor representation (SR) and the default representation (DR). The SR encodes states by the future trajectories they induce, capturing information flow decoupled from reward. The DR builds on this by weighting trajectories with reward, integrating credit-assignment structure into the representation. Eigenvectors of both representations have been used to support a range of downstream tas
The paper builds on established reinforcement learning techniques (successor and default representations) to propose new methods for spatio-temporal abstraction, reflecting ongoing research in making AI systems more efficient and adaptable.
Improved representation learning directly impacts the capabilities of AI agents, offering pathways to more robust and generalized learning, and potentially accelerating the development of autonomous systems.
This research provides foundational insights that could lead to more efficient and powerful reinforcement learning algorithms, fundamentally altering how AI agents perceive and interact with complex environments.
- · AI researchers
- · Reinforcement learning platforms
- · Robotics companies
More sophisticated and adaptable AI agents capable of solving more complex tasks.
Accelerated development of autonomous systems across various sectors, reducing the need for human intervention in certain workflows.
The emergence of new AI-driven product categories and services leveraging highly autonomous agents.
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Read at arXiv cs.LG