What are the most common deep reinforcement learning algorithms and models apart from DQN?

Question

Recently, I have completed Atari Breakout (https://arxiv.org/pdf/1312.5602.pdf) with DQN.

Similar to DQN, what are the most common deep reinforcement learning algorithms and models in 2020? It seems that DQN is outdated and policy gradients are preferred.

Answer 1

There are several common deep reinforcement algorithms and models apart from deep Q networks (or deep Q learning). I will list them below (along with a link to the paper that introduces them or a resource that describes them).

• Proximal policy optimization (PPO)
• Trust region policy optimization (TRPO)
• Deep deterministic policy gradient (DDPG)
• Asynchronous advantage actor-critic (A3C)

For an exhaustive overview of deep RL algorithms and models, you could read Deep Reinforcement Learning (2018) by Yuxi Li.