Rather than building knowledge-intensive models relating algorithm performance to problem features, we base the control decisions on the evolution of solution ...

The algorithm control problem can be viewed as an optimization problem where we want to maximize the improvement in solution cost over time.

Mar 27, 2024 · In this paper, we propose a novel algorithm based on the long and short-term constraints (LSTC) for safe RL.

Jun 24, 2015 · This essay will describe my own views on AI risk, in the hopes of encouraging other researchers to detail their thoughts, as well.

Mar 5, 2025 · Reinforcement learning (RL) is a machine learning type well-suited for control and optimization tasks. In RL, optimal control is approached ...

Jun 27, 2025 · Explore 9 standout reinforcement learning examples that show how AI systems learn, adapt, and solve real-world problems.

Developing a general algorithm that learns to solve tasks across a wide range of applications has been a fundamental challenge in artificial intelligence.

This paper takes model predictive control, a popular optimal control method, as the primary example to survey recent progress that leverages machine learning ...

Some of the autonomous driving tasks where reinforcement learning could be applied include trajectory optimization, motion planning, dynamic pathing, ...

We first came to focus on what is now known as reinforcement learning in late. 1979. We were both at the University of Massachusetts, working on one of.