The manufacturing world is subject to ever-increasing cost optimization pressures.
Maintenance adds to cost and disrupts production; optimized maintenance is therefore of …

Reinforcement learning has emerged as an important approach for autonomous driving. A
reward function is used in reinforcement learning to establish the learned skill objectives …

Myopic exploration policies such as epsilon-greedy, softmax, or Gaussian noise fail to
explore efficiently in some reinforcement learning tasks and yet, they perform well in many …

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 …

With the application of renewable energy in single office buildings, an increasing number of
power grids require building systems coupled with renewable energy to realize off-grid …

Abstract Reward Machines (RMs), originally proposed for specifying problems in
Reinforcement Learning (RL), provide a structured, automata-based representation of a …

Multi-modal learning has made impressive progress in autonomous driving by leveraging
information from multiple sensors. Existing feature fusion methods make decisions by …

Grounding navigational commands to linear temporal logic (LTL) leverages its unambiguous
semantics for reasoning about long-horizon tasks and verifying the satisfaction of temporal …

Reinforcement Learning (RL), bolstered by the expressive capabilities of Deep Neural
Networks (DNNs) for function approximation, has demonstrated considerable success in …

We address the problem of teaching a deep reinforcement learning (RL) agent to follow
instructions in multi-task environments. Instructions are expressed in a well-known formal …