Reinforcement Learning (RL) is a popular machine learning paradigm where intelligent
agents interact with the environment to fulfill a long-term goal. Driven by the resurgence of …

Exploration is critical for good results in deep reinforcement learning and has attracted much
attention. However, existing multi-agent deep reinforcement learning algorithms still use …

Recent research in mechanistic interpretability has attempted to reverse-engineer
Transformer models by carefully inspecting network weights and activations. However, these …

We study the problem of learning control policies for complex tasks given by logical
specifications. Recent approaches automatically generate a reward function from a given …

Self-adaptive systems (SASs) are designed to handle changes and uncertainties through a
feedback loop with four core functionalities: monitoring, analyzing, planning, and execution …

Recent studies on traffic congestion prediction have paved a promising path towards the
reduction of potential economic and environmental loss. However, at the city-wide scale …

A key challenge for reinforcement learning is solving long-horizon planning problems.
Recent work has leveraged programs to guide reinforcement learning in these settings …

In this paper, we propose a new technique based on program synthesis for extracting
information from webpages. Given a natural language query and a few labeled webpages …

随着人工智能技术的发展以及机载任务平台性能的提升, 2021 年无人机技术与应用呈现新的
发展态势. 从无人机政策法规, 自主控制, 反无人机, 应用领域等多方面回眸了2021 …

The goal of programmatic Learning from Demonstration (LfD) is to learn a policy in a
programming language that can be used to control a robot's behavior from a set of user …