Could you explain the Proximal Policy Optimization (PPO) algorithm used in reinforcement learning? Discuss its key components, such as the objective function, how it addresses policy optimization, and the advantages it offers compared to other policy gradient methods. Additionally, illustrate scenarios or environments where PPO might excel or face challenges.

Proximal Policy Optimization (PPO) is a popular algorithm in reinforcement learning (RL) used to optimize policies in a stable and efficient manner. It addresses some issues found in traditional policy gradient methods like high variance and instability.

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