What are the main challenges of reinforcement learning?

What are the main challenges of reinforcement learning?

Reinforcement learning faces several challenges, including sparse rewards, exploration vs. exploitation, and high dimensionality.

Can you explain what sparse rewards mean?

Sparse rewards refer to situations where the agent receives feedback infrequently, making it hard to learn effective strategies.

How does exploration vs. exploitation affect learning?

Exploration involves trying new actions to discover their effects, while exploitation focuses on using known actions that yield high rewards. Balancing both is crucial.

What are some real-world applications of reinforcement learning?

Reinforcement learning is used in robotics, game playing, autonomous vehicles, and personalized recommendations.

Are there any recent advancements in reinforcement learning?

Yes, recent advancements include improved algorithms like Proximal Policy Optimization and applications in complex environments like video games.