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Found 20 papers, 5 papers with code
A Provable Approach for End-to-End Safe Reinforcement Learning
We propose a method, called Provably Lifetime Safe RL (PLS), that integrates offline safe RL with safe policy deployment to address this challenge.
Offline Guarded Safe Reinforcement Learning for Medical Treatment Optimization Strategies
When applying offline reinforcement learning (RL) in healthcare scenarios, the out-of-distribution (OOD) issues pose significant risks, as inappropriate generalization beyond clinical expertise can result in potentially harmful recommendations.
Target Return Optimizer for Multi-Game Decision Transformer
Achieving autonomous agents with robust generalization capabilities across diverse games and tasks remains one of the ultimate goals in AI research.
Vulnerability Mitigation for Safety-Aligned Language Models via Debiasing
Safety alignment is an essential research topic for real-world AI applications.
Flipping-based Policy for Chance-Constrained Markov Decision Processes
We demonstrate that the flipping-based policy can improve the performance of the existing safe RL algorithms under the same limits of safety constraints on Safety Gym benchmarks.
Stepwise Alignment for Constrained Language Model Policy Optimization
This paper formulates human value alignment as an optimization problem of the language model policy to maximize reward under a safety constraint, and then proposes an algorithm, Stepwise Alignment for Constrained Policy Optimization (SACPO).
A Survey of Constraint Formulations in Safe Reinforcement Learning
Safety is critical when applying reinforcement learning (RL) to real-world problems.
Long-term Safe Reinforcement Learning with Binary Feedback
Our theoretical results show that LoBiSaRL guarantees the long-term safety constraint, with high probability.
Verbosity Bias in Preference Labeling by Large Language Models
In recent years, Large Language Models (LLMs) have witnessed a remarkable surge in prevalence, altering the landscape of natural language processing and machine learning.
Bayesian Meta-Learning on Control Barrier Functions with Data from On-Board Sensors
The proposed scheme realizes efficient online synthesis of the controller as shown in the simulation study and provides probabilistic safety guarantees on the resulting controller.
Safe Policy Optimization with Local Generalized Linear Function Approximations
Safe exploration is a key to applying reinforcement learning (RL) in safety-critical systems.
LOA: Logical Optimal Actions for Text-based Interaction Games
We present Logical Optimal Actions (LOA), an action decision architecture of reinforcement learning applications with a neuro-symbolic framework which is a combination of neural network and symbolic knowledge acquisition approach for natural language interaction games.
Neuro-Symbolic Reinforcement Learning with First-Order Logic
Deep reinforcement learning (RL) methods often require many trials before convergence, and no direct interpretability of trained policies is provided.
Reinforcement Learning with External Knowledge by using Logical Neural Networks
Specifically, we propose an integrated method that enables model-free reinforcement learning from external knowledge sources in an LNNs-based logical constrained framework such as action shielding and guide.
Polar Embedding
Particularly, the latest approaches such as hyperbolic embeddings showed significant performance by representing essential meanings in a hierarchy (generality and similarity of objects) with spatial properties (distance from the origin and difference of angles).
Q-learning with Language Model for Edit-based Unsupervised Summarization
Q-learning is leveraged to train the agent to produce proper edit actions.
Safe Reinforcement Learning in Constrained Markov Decision Processes
Safe reinforcement learning has been a promising approach for optimizing the policy of an agent that operates in safety-critical applications.
Failure-Scenario Maker for Rule-Based Agent using Multi-agent Adversarial Reinforcement Learning and its Application to Autonomous Driving
We propose a method for efficiently finding failure scenarios; this method trains the adversarial agents using multi-agent reinforcement learning such that the tested rule-based agent fails.
Safe Exploration in Markov Decision Processes with Time-Variant Safety using Spatio-Temporal Gaussian Process
This paper presents a learning algorithm called ST-SafeMDP for exploring Markov decision processes (MDPs) that is based on the assumption that the safety features are a priori unknown and time-variant.
