Search Results for author:
Found 8 papers, 4 papers with code
Learning Predictive Safety Filter via Decomposition of Robust Invariant Set
This paper presents a theoretical framework that bridges the advantages of both RMPC and RL to synthesize safety filters for nonlinear systems with state- and action-dependent uncertainty.
Absolute Policy Optimization
In recent years, trust region on-policy reinforcement learning has achieved impressive results in addressing complex control tasks and gaming scenarios.
Safety Index Synthesis with State-dependent Control Space
This paper introduces an approach for synthesizing feasible safety indices to derive safe control laws under state-dependent control spaces.
State-wise Constrained Policy Optimization
In particular, we introduce the framework of Maximum Markov Decision Process, and prove that the worst-case safety violation is bounded under SCPO.
GUARD: A Safe Reinforcement Learning Benchmark
Due to the diversity of algorithms and tasks, it remains difficult to compare existing safe RL algorithms.
State-wise Safe Reinforcement Learning: A Survey
Despite the tremendous success of Reinforcement Learning (RL) algorithms in simulation environments, applying RL to real-world applications still faces many challenges.
AutoCost: Evolving Intrinsic Cost for Zero-violation Reinforcement Learning
Results show that the converged policies with intrinsic costs in all environments achieve zero constraint violation and comparable performance with baselines.
Provably Safe Tolerance Estimation for Robot Arms via Sum-of-Squares Programming
Tolerance estimation problems are prevailing in engineering applications.
