To introduce a corrective force, AI developers frequently use what is called reinforcement learning from human feedback (RLHF). Essentially they are putting a human thumb on the scale as the ...

DeepSeek-R1’s Monday release has sent shockwaves through the AI community, disrupting assumptions about what’s required to achieve cutting-edge AI performance. This story focuses on exactly ...

This paper introduces four co-simulation platforms for testing deep reinforcement learning (DRL)-based control solutions in power systems. The first one is to connect the off-the-shelf Matlab DRL ...

First, authors provide the mathematical model describing a multi-impulse linear rendezvous problem and the RL algorithms used, and present the RL-based approach to rendezvous design. For the multi ...

On Oct. 17, Coco Krumme, an applied mathematician and writer, spoke about her new book in conversation with Jonathan Zittrain, a professor of International Law and Computer Science, at the Harvard ...

This chapter introduced temporal-difference (TD) learning, and showed how it can be applied to the reinforcement learning problem. The TD control methods are classified according to whether they deal ...

This dissertation seeks to compare the differences between using the state-of-the-art deep reinforcement learning algorithm Proximal Policy Optimization to control a quadcopter against using PID ...

Reinforcement learning was part of the algorithms that were integral to achieving breakthrough results with chess, protein folding and Atari games. Likewise, OpenAI trained deep reinforcement ...