Scalable Signal Temporal Logic Guided Reinforcement Learning via Value Function Space Optimization

The integration of reinforcement learning (RL) and formal methods has emerged as a promising framework for solving long-horizon planning problems. Conventional approaches typically involve abstraction of the state and action spaces and manually created labeling functions or predicates. However, the efficiency of these approaches deteriorates as the tasks become increasingly complex, which results in exponential growth in the size of labeling functions or predicates. To address these issues, we propose a scalable model-based RL framework, called VFSTL, which schedules pre-trained skills to follow unseen STL specifications without using hand-crafted predicates. Given a set of value functions obtained by goal-conditioned RL, we formulate an optimization problem to maximize the robustness value of Signal Temporal Logic (STL) defined specifications, which is computed using value functions as predicates. To further reduce the computation burden, we abstract the environment state space into the value function space (VFS). Then the optimization problem is solved by Model-Based Reinforcement Learning. Simulation results show that STL with value functions as predicates approximates the ground truth robustness and the planning in VFS directly achieves unseen specifications using data from sensors.

SEGAN: semi-supervised learning approach for missing data imputation

In many practical real-world applications, data missing is a very common phenomenon, making the development of data-driven artificial intelligence theory and technology increasingly difficult. Data completion is an important method for missing data preprocessing. Most existing miss-ing data completion models directly use the known information in the missing data set but ignore the impact of the data label information contained in the data set on the missing data completion model. To this end, this paper proposes a missing data completion model SEGAN based on semi-supervised learning, which mainly includes three important modules: generator, discriminator and classifier. In the SEGAN model, the classifier enables the generator to make more full use of known data and its label information when predicting missing data values. In addition, the SE-GAN model introduces a missing hint matrix to allow the discriminator to more effectively distinguish between known data and data filled by the generator. This paper theoretically proves that the SEGAN model that introduces a classifier and a missing hint matrix can learn the real known data distribution characteristics when reaching Nash equilibrium. Finally, a large number of experiments were conducted in this article, and the experimental results show that com-pared with the current state-of-the-art multivariate data completion method, the performance of the SEGAN model is improved by more than 3%.

LaMPilot: An Open Benchmark Dataset for Autonomous Driving with Language Model Programs

We present LaMPilot, a novel framework for planning in the field of autonomous driving, rethinking the task as a code-generation process that leverages established behavioral primitives. This approach aims to address the challenge of interpreting and executing spontaneous user instructions such as "overtake the car ahead," which have typically posed difficulties for existing frameworks. We introduce the LaMPilot benchmark specifically designed to quantitatively evaluate the efficacy of Large Language Models (LLMs) in translating human directives into actionable driving policies. We then evaluate a wide range of state-of-the-art code generation language models on tasks from the LaMPilot Benchmark. The results of the experiments showed that GPT-4, with human feedback, achieved an impressive task completion rate of 92.7% and a minimal collision rate of 0.9%. To encourage further investigation in this area, our code and dataset will be made available.