What are some common mistakes in reinforcement learning?

What are some common mistakes in reinforcement learning?

Some common mistakes include not properly defining the reward function, overfitting to training data, and ignoring exploration.

Can you explain why defining the reward function is so important?

Absolutely! The reward function guides the agent's learning process. If it's poorly defined, the agent may learn unintended behaviors.

What do you mean by overfitting to training data?

Overfitting occurs when the model learns the training data too well, failing to generalize to new, unseen situations.

How can I avoid these mistakes in my projects?

To avoid these mistakes, ensure a clear reward structure, use regularization techniques, and implement robust exploration strategies.