Reinforcement learning focuses on rewarding desired AI actions and punishing undesired ones. Common RL algorithms include State-action-reward-state-action, Q-learning, and Deep-Q networks. RL ...

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

Such frameworks, including benchmark test suites, have paved the way for more rigorous and systematic performance assessments of multi-objective deep reinforcement learning algorithms [3].

The application of Deep Reinforcement Learning (DRL) in economics has been an area of active research in recent years. A number of recent works have shown how deep reinforcement learning can be used ...

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

In my work as an AI researcher, I use reinforcement learning to create AI algorithms that learn how to solve puzzles such as the Rubik’s Cube.

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.

By optimizing reinforcement-learning algorithms, DeepMind uncovered new details about how dopamine helps the brain learn.