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 ...

Deep reinforcement learning has helped solve very complicated challenges and will continue to be an important interest for the AI community.

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

In this course we introduce the fundamentals of Deep Reinforcement Learning from scratch starting from its roots in Dynamic Programming and optimal control, and ending with some of the most popular ...

Deep Reinforcement Learning (DRL) is a subfield of machine learning that combines neural networks with reinforcement learning techniques to make decisions in complex environments. It has been ...

Rather than generating potential outcomes based on historical data, deep reinforcement learning teaches AI agents and machines with the time-tested "carrot and stick" method.

This study seeks to construct a basic reinforcement learning-based AI-macroeconomic simulator. We use a deep RL (DRL) approach (DDPG) in an RBC macroeconomic model. We set up two learning scenarios, ...

In this way Agent57 is similar to AlphaZero, DeepMind’s deep reinforcement learning algorithm, which can learn to play chess, Go, and shogi—but again, not all at once.

A new technique from Stanford researchers creates AI virtual agents that can evolve both in their physical structure and learning capacities.