Deep Model-Based Reinforcement Learning for High-Dimensional Problems, a Survey

Understand

Deep reinforcement learning has shown remarkable success in the past few years.

• Highly complex sequential decision making problems have been solved in tasks such as game playing and robotics.
• Unfortunately, the sample complexity of most deep reinforcement learning methods is high, precluding their use in some important applications.
• Model-based reinforcement learning creates an explicit model of the environment dynamics to reduce the need for environment samples.