Search Results for author:
Found 6 papers, 2 papers with code
Safeguarded Progress in Reinforcement Learning: Safe Bayesian Exploration for Control Policy Synthesis
This paper addresses the problem of maintaining safety during training in Reinforcement Learning (RL), such that the safety constraint violations are bounded at any point during learning.
Mission-driven Exploration for Accelerated Deep Reinforcement Learning with Temporal Logic Task Specifications
We consider robots with unknown stochastic dynamics operating in environments with unknown geometric structure.
Decoding In-Context Learning: Neuroscience-inspired Analysis of Representations in Large Language Models
In this work, we investigate how LLM embeddings and attention representations change following in-context-learning, and how these changes mediate improvement in behavior.
ALLURE: Auditing and Improving LLM-based Evaluation of Text using Iterative In-Context-Learning
From grading papers to summarizing medical documents, large language models (LLMs) are evermore used for evaluation of text generated by humans and AI alike.
LCRL: Certified Policy Synthesis via Logically-Constrained Reinforcement Learning
LCRL is a software tool that implements model-free Reinforcement Learning (RL) algorithms over unknown Markov Decision Processes (MDPs), synthesising policies that satisfy a given linear temporal specification with maximal probability.
Certified Reinforcement Learning with Logic Guidance
Reinforcement Learning (RL) is a widely employed machine learning architecture that has been applied to a variety of control problems.
