AWS Certified Cloud Practitioner

Explanation:

Domain randomization is a technique in reinforcement learning where the training environment is varied with random parameters (such as different layouts, textures, lighting conditions, etc.) during training. This approach helps the model learn robust policies that can generalize to unseen environments because it has been exposed to a wide range of variations during training.

• Option A (Train only in a fixed environment) is incorrect because training in a fixed environment leads to overfitting - the model learns to perform well only in that specific environment but fails to generalize to new, unseen environments.

• Option B (Reduce exploration) is incorrect because exploration is essential for discovering optimal policies. Reducing exploration might stabilize behavior in the training environment but doesn't help with generalization to new environments.

• Option C (Add domain randomization) is correct as explained above.

• Option D (Remove reward penalties) is incorrect because reward penalties often serve as important constraints that guide the learning process. Removing them might make the policy more flexible but doesn't necessarily improve generalization to unseen environments.

Key Concept: Domain randomization is a powerful technique for improving generalization in reinforcement learning by exposing the model to diverse environmental conditions during training, making it more robust and adaptable to real-world variations.