Reinforcement learning in RTS gaming
Here we will discuss how reinforcement learning algorithms can be implemented to solve the real-time strategy gaming problem. Let's recall the basic components of reinforcement learning again, they are are follows:
- States S
- Actions A
- Rewards R
- Transition model (if on-policy, not required for off-policy learning)
If these components are perceived and processed by the sensors present on the learning agent while receiving signals from the given gaming environment, then a reinforcement learning algorithm can be successfully applied. The signals perceived by the sensors can be processed to form the current environment state, predict the action as per the state information, and receive feedback, that is, reward where the action taken was good or bad. This updates that state-action pair value that is, reinforces its learning as per the feedback received.
Moreover, the higher dimension state and action spaces can be encoded to compact lower dimensions by using deep...
