The reason why reinforcement learning stands out relative to other AI approaches are as follows:
- Avoids hand coded rule-based approach.
- Reinforcement learning doesn't require any need to store the game's specific rules. A reinforcement learning agent learns over multiple interactions and reinforces its understanding to act in an environment each time it interacts with the environment.
- For high-dimensional state-action spaces, a neural network can be used as a function approximator to derive optimal actions.
- Always explores different policies to find the optimal one.
- Reinforcement learning has been applied to various domains that require state-action planning, such as robotics, self driving cars, and so on.
- Moreover, ...