- In reinforcement learning, the agent typically interacts with an environment and receives rewards or penalties based on its actions.
- The self-state is the representation of the agent's internal state, which consists of its current observations, memories, and internal variables relevant to the task.
- It is the input to the agent's policy or decision-making function, determining its actions.
- The self-state evolves as the agent interacts with the environment.