New Framework Improves Quality-Diversity Reinforcement Learning Through Structure-Conditioned Actor-Critic Branches
Researchers have proposed SV-QD-RL, a structure-value coupled framework for quality-diversity reinforcement learning that represents each candidate policy as a structure-conditioned actor-critic branch. The method addresses a gap in existing QD-RL approaches, which largely left the learning branches that generate candidate policies unexplored. Experiments on MuJoCo continuous-control tasks suggest the framework produces policy repertoires that are both high-performing and behaviorally diverse.
A preprint submitted to arXiv introduces SV-QD-RL, a framework designed to improve quality-diversity reinforcement learning (QD-RL) by tightly coupling actor structure with branch-specific value learning. Each candidate policy is represented as a structure-conditioned actor-critic branch comprising an actor, a structural mask, a branch-specific critic, a replay state, and evaluation attributes such as behavior, return, sparsity, and value profile. The structural mask constrains the actor to a defined subspace, while the branch-specific critic and replay state shape each branch's value-learning trajectory independently. A branch-aware QD archive then selects and retains branches based on behavioral quality, structural footprint, and value-profile information. Ablation studies indicate that structural conditioning, critic differentiation, and memory-consistent refinement each contribute complementarily to behavioral specialization. Schedule-aware repertoire evaluation further demonstrates that the learned archive can provide selectable policy alternatives when behavior-level requirements change over time. The authors argue these results establish structure-value coupling as an effective mechanism for generating diverse and high-quality policy repertoires.
What's missing
The paper is a preprint and has not yet undergone peer review. Comparisons are limited to MuJoCo continuous-control benchmarks, leaving open questions about generalizability to discrete-action or real-world environments.
What different sources said
- arXiv cs.LGCenter
Informed Asymmetric Actor-Critic: Leveraging Privileged Signals Beyond Full-State Access
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