How does the concept of reinforcement learning apply to dynamic and uncertain environments, and what are the challenges in designing effective reward functions?

Reinforcement learning (RL) applies to dynamic and uncertain environments by training agents to make decisions that maximize cumulative rewards over time. The agent learns through trial and error, receiving feedback from the environment. Designing effective reward functions is challenging as they must balance immediate and long-term goals, avoid unintended behaviors, and provide sufficient feedback to guide the learning process. Properly designed reward functions are critical for achieving desired outcomes in RL applications.