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This paper introduces adaptive probabilistic shielding for safe reinforcement learning, where the shield is computed from an online learned MDP model, adapting as the model becomes more accurate.
SteinGate introduces a distributional safety certificate using Kernelized Stein Discrepancy to detect rare catastrophic tail events in safe reinforcement learning, dynamically adapting policy updates to reduce constraint violations while maintaining competitive returns.
This work-in-progress paper proposes embedding a differentiable physics model into the PPO actor loss function to penalize anticipated safety violations in reinforcement learning, evaluated on a simulated 1-DoF helicopter system. The physics-informed soft regularizations reduce constraint violations while maintaining reliable target tracking.
This paper proposes Constraint-Sensitive Policy Optimization (CSPO), a first-order primal-dual method for safe reinforcement learning that incorporates local constraint sensitivity to improve safety recovery and reduce oscillations near safety boundaries, achieving higher constrained returns on navigation and locomotion benchmarks.
A method for contract-based compositional shielding that ensures global safety in multi-agent reinforcement learning without centralized runtime control, using local LTL obligations and a multi-armed bandit to optimize team reward.
Introduces a novel shielding framework for robust Markov decision processes (RMDPs) that formally guarantees safety under uncertain transition dynamics, proving soundness and optimality. The approach combines with PAC guarantees for learned models, enabling safe reinforcement learning in unknown environments.