Question: Consider an agent interacting with a Markov Decision Process ( MDP ) , where the goal is to solve it using Q - learning. Assume

Consider an agent interacting with a Markov Decision Process (MDP), where the goal is to solve it using Q-learning. Assume the agent's rewards in human-in-the-loop problems are sparse and provided intermittently over the course of one loop.
How does sparsity in rewards affect the convergence rate of the method?
What strategies can you propose to address the issues caused by sparse rewards?
What are other potential challenges related to human-in-the-loop problems, and what solutions can you suggest for these?

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