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How to make robots grow intelligently through PPO: the secret behind success!
In today's era of rapid technological development, artificial intelligence has become an indispensable part of many industries. Among them, the importance of reinforcement learning (RL) cannot be underestimated as a technology that allows agents to learn autonomously and improve their decision-making capabilities. Among various reinforcement learning algorithms, Proximal Policy Optimization (PPO) has quickly become a mainstream choice since its advent in 2017 due to its excellent performance and stability. This article will take an in-depth look at how PPO works, how it can be successful in a variety of applications, and explore the secrets behind it.
Development History
The predecessor of PPO is Trust Region Policy Optimization (TRPO) proposed by John Schulman in 2015. TRPO solves the problem of close competition by controlling the KL divergence between the old policy and the new policy. However, its high computational complexity makes it difficult and costly to implement on large-scale problems. In 2017, Schulman proposed PPO to address the complexity of TRPO, simplifying the process and improving performance. The key to PPO is its tailoring mechanism, which limits the scope of changes in new policies to avoid training instability caused by excessive changes.
Main theories and principles
The core of PPO lies in the training of its policy function. When the agent acts in the environment, it selects its next action based on the current input in a random sampling manner, with the goal of maximizing the accumulated reward. The key element in this process is the so-called advantage function. This function is used to evaluate the effectiveness of the current action compared to other possible actions to provide a basis for decision-making.
The advantage function is defined as A = Q - V, where Q is the sum of discounted returns and V is the baseline forecast.
Advantage function and proportional function
In PPO, the dominance function helps verify whether the agent's actions are better than the baseline and affects future strategy choices. The proportion function is used to estimate the difference between the current policy and the old policy, which is crucial to ensure the controllability of policy updates. The strategy update adopted by PPO is based on the product of these two functions, and this design keeps the algorithm stable during the training process.
Objective function of PPO
The objective function of PPO mainly considers the expected value of policy update, reflecting a conservative learning method. Specifically, PPO takes into account the minimum values of the proportion function and the advantage function when calculating the target to ensure that the agent does not undergo large-scale changes when updating the strategy. The core of this design is to protect the agent from deviating from the optimal strategy due to unnecessary changes.
Through the tailoring mechanism, PPO will significantly reduce unstable policy updates and ensure that agents maintain the best path during the learning process.
Advantages of PPO
Compared with other reinforcement learning algorithms, PPO shows significant advantages, including simplicity, stability and sample efficiency. PPO can achieve similar results to TRPO with fewer resources and significantly reduces computational complexity, which makes PPO more suitable for large-scale problems. In addition, the use of PPO can also be adapted to a variety of tasks without excessive hyperparameter adjustment.
The advantage of sample efficiency enables PPO to achieve good results with less training data when dealing with high-dimensional complex tasks.
Application Scope
Since 2018, PPO has been widely adopted in multiple application scenarios. In robot control, video games, and especially Dota 2 competitions, PPO has demonstrated its powerful learning capabilities. In these projects, PPO not only improved the robot's control accuracy, but also greatly improved the learning efficiency of the algorithm.
Conclusion
In the development of reinforcement learning, PPO is undoubtedly a landmark achievement. Its simplicity, efficiency and stability make it an important tool for developing intelligent robots. However, we also need to think about, with the advancement of technology, can we develop more efficient learning algorithms in the future to promote the intelligentization process of robots?
