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.