Intrinsic Motivation in RL is the use of internally generated reward signals to drive exploration — augmenting external (task) rewards with intrinsic rewards based on novelty, curiosity, surprise, or competence, enabling the agent to explore effectively even without external reward.

Intrinsic Reward Types

• Curiosity: Reward for encountering states that are hard to predict — prediction error as reward.
• Count-Based: Reward inversely proportional to visitation count — visit novel states.
• Information Gain: Reward for actions that reduce uncertainty about the environment model.
• Empowerment: Reward for states where the agent has maximum control over future outcomes.

Why It Matters

• Sparse Rewards: Many real-world tasks have extremely sparse external rewards — intrinsic motivation enables learning.
• Exploration: Intrinsic rewards drive systematic exploration of the environment — avoids random wandering.
• Autonomy: Enables agents to learn useful skills without any external reward — pre-training for downstream tasks.

Intrinsic Motivation is self-driven curiosity — generating internal rewards to explore and learn even without external feedback.