CPSS projects cumulative safety constraints into time-varying per-state thresholds for online action shielding in nonstationary RL, providing per-state guarantees and cumulative bounds.
A survey of constraint formulations in safe reinforcement learning
4 Pith papers cite this work. Polarity classification is still indexing.
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LILAC+ combines context-based, adaptation-speed, and budget-to-state safety constraints to reduce violations in continual RL under nonstationary conditions, demonstrated in simulated driving tasks.
PRL-PUTS casts utility-weight tuning as a one-step value-based RL task and uses scalarization-parameter Pareto sweeping at inference time to generate and govern a family of policies, reporting +0.13% lift in successful sessions on Pinterest Homefeed.
A literature survey that unifies fragmented work on attacks, defenses, evaluations, and deployment challenges for Vision-Language-Action models in robotics.
citing papers explorer
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From Cumulative Constraints to Adaptive Runtime Safety Control for Nonstationary Reinforcement Learning
CPSS projects cumulative safety constraints into time-varying per-state thresholds for online action shielding in nonstationary RL, providing per-state guarantees and cumulative bounds.
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Safe Continual Reinforcement Learning under Nonstationarity via Adaptive Safety Constraints
LILAC+ combines context-based, adaptation-speed, and budget-to-state safety constraints to reduce violations in continual RL under nonstationary conditions, demonstrated in simulated driving tasks.
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A Production-Ready RL Framework for Personalized Utility Tuning with Pareto Sweeping in Pinterest Recommender Systems
PRL-PUTS casts utility-weight tuning as a one-step value-based RL task and uses scalarization-parameter Pareto sweeping at inference time to generate and govern a family of policies, reporting +0.13% lift in successful sessions on Pinterest Homefeed.
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Vision-Language-Action Safety: Threats, Challenges, Evaluations, and Mechanisms
A literature survey that unifies fragmented work on attacks, defenses, evaluations, and deployment challenges for Vision-Language-Action models in robotics.