What are the main challenges in reinforcement learning?

What are the main challenges in reinforcement learning?

Some key challenges include sample efficiency, exploration vs. exploitation, and dealing with sparse rewards.

Can you explain sample efficiency?

Sample efficiency refers to how effectively an algorithm learns from a limited number of training samples.

What about exploration vs. exploitation?

It's the dilemma of choosing between exploring new actions to find better rewards or exploiting known actions that yield high rewards.

How do sparse rewards affect learning?

Sparse rewards can make it difficult for the agent to learn since it receives feedback infrequently, complicating the learning process.

Are there any techniques to overcome these challenges?

Yes, techniques like reward shaping, hierarchical reinforcement learning, and using prior knowledge can help address these challenges.