Author: Zen and the Art of Computer Programming
1 Introduction
With the vigorous development of the field of artificial intelligence in recent years, reinforcement learning (RL) has been recognized and applied to the field of artificial intelligence by more and more people. Today, RL can already handle many complex problems, such as autonomous driving, robot control, etc. In the past period of time, I have been wanting to share with you the future development direction of RL in artificial intelligence, so I wanted to make this topic a professional technical blog article.
DQN (Deep Q-Network) is a reinforcement learning algorithm that uses a neural network to approximate the Q function, and uses experience playback and target networks to improve learning stability.
DDPG (Deep Deterministic Policy Gradient) is an unbiased estimation algorithm based on the Actor-Critic architecture, used to solve continuous action control problems. Its core idea is to use Double Q-Network to train Policy Network and Value Network, and use experience replay and target network to improve learning stability.
This article will discuss reinforcement learning from the following aspects:
① Large-scale deployment: How to accelerate training and application of RL through GPU;
② Small-scale deployment: How to quickly develop, go online and deploy RL models;
③ Model combination method: How to generate a more accurate prediction model through RL model design?
④ Evolution and evolution: How to make the RL model better adapt to environmental changes?
⑤ Multi-task collaboration: How to use RL to achieve multi-task collaborative optimization?
⑥ Online learning: How to make the RL model learn new knowledge in real time without relying on offline training?