What are sparse rewards in reinforcement learning?

What are sparse rewards in reinforcement learning?

Sparse rewards refer to situations in reinforcement learning where the agent receives feedback infrequently, making learning challenging.

Can you give an example of a task with sparse rewards?

Sure! A classic example is training a robot to navigate a maze, where it only receives a reward upon reaching the exit.

How do agents learn in environments with sparse rewards?

Agents often use techniques like reward shaping or intrinsic motivation to encourage exploration and learning despite sparse feedback.

What are some strategies to handle sparse rewards?

Strategies include using hierarchical reinforcement learning, employing curiosity-driven exploration, and leveraging prior knowledge.