Proximal Policy Optimization: Principles and Applications
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Proximal Policy Optimization, or PPO, is a reinforcement learning algorithm that revolutionized policy gradient methods. In reinforcement learning, an agent interacts with an environment by taking actions and receiving rewards. PPO addresses the key challenge of balancing exploration versus exploitation while maintaining training stability. Developed in 2017, PPO built upon earlier methods like REINFORCE and TRPO to create a more practical and effective algorithm.
Policy gradient methods form the foundation of PPO. The policy gradient theorem shows how to compute gradients of the expected return with respect to policy parameters. The REINFORCE algorithm uses this theorem to update policies, but suffers from high variance and sample inefficiency. These problems manifest as unstable learning curves with large fluctuations in performance, making training unreliable and slow.
Trust regions provide a principled approach to constraining policy updates. The idea is to define safe zones around the current policy where updates are likely to improve performance. TRPO implements this using KL divergence constraints, ensuring that the new policy doesn't deviate too much from the old one. However, TRPO requires computationally expensive second-order optimization, making it impractical for many applications.
PPO simplifies the trust region approach through a clipped surrogate objective. The key innovation is the probability ratio r-theta, which measures how much the new policy differs from the old one. PPO clips this ratio between 1 minus epsilon and 1 plus epsilon, typically using epsilon values of 0.1 to 0.3. This prevents destructively large policy updates while being computationally much simpler than TRPO, requiring only first-order optimization.