A Survey of Deep Reinforcement Learning in Video Games

Understand

Deep reinforcement learning (DRL) has made great achievements since proposed.

• Generally, DRL agents receive high-dimensional inputs at each step, and make actions according to deep-neural-network-based policies.
• This learning mechanism updates the policy to maximize the return with an end-to-end method.
• In this paper, we survey the progress of DRL methods, including value-based, policy gradient, and model-based algorithms, and compare their main techniques and properties.