Reinforcement learning and simulation are essential to solving the constraints and novel challenges that take place in factories and supply chains.

For example, a constrained multi-objective deep reinforcement learning approach has been introduced to optimise complex industrial processes, such as temperature field management in zinc oxide ...

To this end, reinforcement learning has been particularly useful with robotics. For example, OpenAI has used this technique for a robotic arm that was able to solve the Rubik’s cube.

An AI strategy proven adept at board games like Chess and Go, reinforcement learning, has now been adapted for a powerful protein design program. The results show that reinforcement learning can ...

This course is about reinforcement learning, covering the fundamental concepts of reinforcement learning framework and solution methods. The focus is on the underlying methodology as well as practical ...

How reinforcement learning with human feedback helps ensure that businesses are building ethical generative AI models.

What is Reinforcement Learning? At the core of reinforcement learning is the concept that the optimal behavior or action is reinforced by a positive reward.

Reinforcement learning techniques could be the keys to integrating robots — who use machine learning to output more than words — into the real world.

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 ...