A survey on explainable reinforcement learning: Concepts, algorithms, challenges
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 …
agents interact with the environment to fulfill a long-term goal. Driven by the resurgence of …
Cooperative exploration for multi-agent deep reinforcement learning
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 …
attention. However, existing multi-agent deep reinforcement learning algorithms still use …
Learning transformer programs
Recent research in mechanistic interpretability has attempted to reverse-engineer
Transformer models by carefully inspecting network weights and activations. However, these …
Transformer models by carefully inspecting network weights and activations. However, these …
Compositional reinforcement learning from logical specifications
K Jothimurugan, S Bansal… - Advances in Neural …, 2021 - proceedings.neurips.cc
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 …
specifications. Recent approaches automatically generate a reward function from a given …
Generative AI for Self-Adaptive Systems: State of the Art and Research Roadmap
Self-adaptive systems (SASs) are designed to handle changes and uncertainties through a
feedback loop with four core functionalities: monitoring, analyzing, planning, and execution …
feedback loop with four core functionalities: monitoring, analyzing, planning, and execution …
Integrating the traffic science with representation learning for city-wide network congestion prediction
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 …
reduction of potential economic and environmental loss. However, at the city-wide scale …
Program synthesis guided reinforcement learning for partially observed environments
A key challenge for reinforcement learning is solving long-horizon planning problems.
Recent work has leveraged programs to guide reinforcement learning in these settings …
Recent work has leveraged programs to guide reinforcement learning in these settings …
Web question answering with neurosymbolic program synthesis
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 …
information from webpages. Given a natural language query and a few labeled webpages …
2021 年无人机热点回眸
段海滨, 何杭轩, 赵彦杰, 王寅, 牛轶峰, 袁莞迈… - 科技导报, 2022 - kjdb.org
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 …
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 …
Programming-by-demonstration for long-horizon robot tasks
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 …
programming language that can be used to control a robot's behavior from a set of user …