arXiv:2606.08735v1 Announce Type: new Abstract: Quality-diversity reinforcement learning (QD-RL) aims to construct policy repertoires that contain both high-performing and behaviorally diverse policies. Existing QD-RL methods mainly diversify policy instances after rollout evaluation or use learned value information to improve policy quality and behavior targeting, while the learning branches that generate candidate policies remain less explored. This paper proposes SV-QD-RL, a structure-value coupled framework that represents each candidate as a structure-conditioned actor-critic branch. Each
The paper introduces a novel framework for Quality-Diversity Reinforcement Learning (QD-RL) that is emerging as a critical area for building more robust and versatile AI.
Improving QD-RL methods is crucial for developing AI systems capable of both high performance and behavioral diversity, a key step towards more generalized and adaptive AI.
The proposed SV-QD-RL framework could lead to more efficient and effective ways of generating diverse and high-performing AI policies, impacting the design and capabilities of future autonomous systems.
- · AI researchers
- · Robotics developers
- · Autonomous system designers
- · Existing QD-RL methods (potentially)
Improved performance and adaptability of AI agents in complex environments.
Accelerated development of AI systems capable of handling unexpected variations and novel situations effectively.
Enhanced AI robustness could lead to broader adoption in safety-critical applications, further accelerating deployment of AI agents.
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Read at arXiv cs.AI