Which deep reinforcement learning algorithm is appropriate for my problem? My task is to solve an optimization problem with deep reinforcement learning. I read about several algorithms like DQN, PPO, DDPG, and A2C/A3C but use cases always seem to be problems like video games (sparse rewards, etc.) or robotics (continuous action spaces, etc.). Since my problem is an optimization issue, I wonder which algorithm is appropriate for my setting:


limited number of discrete actions (like 20)
high-dimensional states (like 250 values)
instant reward after every single action (not only at the end of an episode)
a single action can affect the state quite a lot


There's no "goal" like in a video game, an episode ends after a certain number of actions. I'm not quite sure which algorithm is appropriate for my use case.
Theoretically video games and robotics problems are also about optimization(getting maximum reward). So, just like other reinforcement learning problems, I would expect PPO to be the most efficient in your case too. I don't think a "goal" is necessary for rl, all you need is the rewards.