Safe Reinforcement Learning
76 papers with code • 0 benchmarks • 1 datasets
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safe-control-gym
Latest papers with no code
Safe Reinforcement Learning on the Constraint Manifold: Theory and Applications
Videos of the real robot experiments are available on the project website (https://puzeliu. github. io/TRO-ATACOM).
Long and Short-Term Constraints Driven Safe Reinforcement Learning for Autonomous Driving
Reinforcement learning (RL) has been widely used in decision-making tasks, but it cannot guarantee the agent's safety in the training process due to the requirements of interaction with the environment, which seriously limits its industrial applications such as autonomous driving.
Safe Reinforcement Learning for Constrained Markov Decision Processes with Stochastic Stopping Time
In this paper, we present an online reinforcement learning algorithm for constrained Markov decision processes with a safety constraint.
From Representational Harms to Quality-of-Service Harms: A Case Study on Llama 2 Safety Safeguards
Despite growing mitigation efforts to develop safety safeguards, such as supervised safety-oriented fine-tuning and leveraging safe reinforcement learning from human feedback, multiple concerns regarding the safety and ingrained biases in these models remain.
Temporal Logic Specification-Conditioned Decision Transformer for Offline Safe Reinforcement Learning
Offline safe reinforcement learning (RL) aims to train a constraint satisfaction policy from a fixed dataset.
Safety Optimized Reinforcement Learning via Multi-Objective Policy Optimization
The advantage of the Safety Optimized RL (SORL) algorithm compared to the traditional Safe RL algorithms is that it omits the need to constrain the policy search space.
Multi-Constraint Safe RL with Objective Suppression for Safety-Critical Applications
Safe reinforcement learning tasks with multiple constraints are a challenging domain despite being very common in the real world.
Provable Traffic Rule Compliance in Safe Reinforcement Learning on the Open Sea
We introduce an efficient verification approach that determines the compliance of actions with respect to the COLREGS formalized using temporal logic.
Multi-Network Constrained Operational Optimization in Community Integrated Energy Systems: A Safe Reinforcement Learning Approach
The integrated community energy system (ICES) has emerged as a promising solution for enhancing the efficiency of the distribution system by effectively coordinating multiple energy sources.
A Safe Reinforcement Learning driven Weights-varying Model Predictive Control for Autonomous Vehicle Motion Control
However, a single parameter set may not deliver the most optimal closed-loop control performance when the context of the MPC operating conditions changes during its operation, urging the need to adapt the cost function weights at runtime.
