Optimized Proximal Policy Algorithm (PPO)

Proximal Policy Optimization (PPO) is a reinforcement learning algorithm designed to solve the problems of unstable training and low sample efficiency in deep reinforcement learning. The PPO algorithm is based on policy gradient and trains the agent by optimizing the policy to maximize long-term returns. Compared with other algorithms, PPO has the advantages of simplicity, efficiency, and stability, so it is widely used in academia and industry. PPO improves the training process through two key concepts: proximal policy optimization and shearing the objective function. Proximal policy optimization maintains training stability by limiting the size of policy updates to ensure that each update is within an acceptable range. The shear objective function is the core idea of the PPO algorithm. When updating the policy, it uses the shear objective function to constrain the magnitude of the policy update to avoid excessive updates that lead to unstable training. The PPO algorithm shows good performance in practice

In the PPO algorithm, the strategy is represented by a neural network. Neural networks accept the current state as input and output a probability value for each available action. At each time step, the agent chooses an action based on the probability distribution output by the policy network. The agent then performs the action and observes the next state and reward signal. This process will be repeated until the mission is completed. By repeating this process, the agent can learn how to choose the optimal action based on the current state to maximize the cumulative reward. The PPO algorithm balances the exploration and utilization of strategies by optimizing the step size and update amplitude of strategy updates, thereby improving the stability and performance of the algorithm.

The core idea of the PPO algorithm is to use the proximal policy optimization method for policy optimization to avoid the problem of performance degradation caused by too aggressive policy updates. Specifically, the PPO algorithm adopts a shear function to limit the difference between the new policy and the old policy within a given range. This shear function can be linear, quadratic or exponential, etc. By using the shear function, the PPO algorithm can balance the intensity of policy updates, thereby improving the stability and convergence speed of the algorithm. This method of proximal policy optimization enables the PPO algorithm to show good performance and robustness in reinforcement learning tasks.

The core of the PPO (Proximal Policy Optimization) algorithm is to improve the adaptability of the policy in the current environment by updating the parameters of the policy network. Specifically, the PPO algorithm updates the parameters of the policy network by maximizing the PPO objective function. This objective function consists of two parts: one is the optimization goal of the strategy, which is to maximize long-term returns; the other is a constraint term used to limit the difference between the updated strategy and the original strategy. In this way, the PPO algorithm can effectively update the parameters of the policy network and improve the performance of the policy while ensuring stability.

In the PPO algorithm, in order to constrain the difference between the updated policy and the original policy, we use a technique called clipping. Specifically, we compare the updated policy with the original policy and limit the difference between them to no more than a small threshold. The purpose of this pruning technology is to ensure that the updated policy will not be too far away from the original policy, thereby avoiding excessive updates during the training process, which will lead to training instability. Through clipping techniques, we are able to balance the magnitude of updates and ensure training stability and convergence.

The PPO algorithm utilizes empirical data by sampling multiple trajectories, thereby improving sample efficiency. During training, multiple trajectories are sampled and then used to estimate the long-term reward and gradient of the policy. This sampling technique can reduce the variance during training, thereby improving the stability and efficiency of training.

The optimization goal of the PPO algorithm is to maximize the expected return, where return refers to the cumulative reward obtained after executing a series of actions starting from the current state. The PPO algorithm uses a method called "importance sampling" to estimate the policy gradient, that is, for the current state and action, compare the probability ratio of the current policy and the old policy, use it as a weight, multiply it by the reward value, and finally get the policy gradient.

In short, the PPO algorithm is an efficient, stable, and easy-to-implement strategy optimization algorithm suitable for solving continuous control problems. It uses proximal policy optimization methods to control the magnitude of policy updates, and uses importance sampling and value function clipping methods to estimate policy gradients. The combination of these techniques makes the PPO algorithm perform well in a variety of environments, making it one of the most popular reinforcement learning algorithms currently.

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