We propose a new family of policy gradient methods for reinforcement learning, which alternate between sampling data through interaction with the environment, and optimizing a "surrogate" objective function using stochastic gradient ascent. Whereas standard policy gradient methods perform one gradient update per data sample, we propose a novel objective function that enables multiple epochs of minibatch updates. The new methods, which we call proximal policy optimization (PPO), have some of the benefits of trust region policy optimization (TRPO), but they are much simpler to implement, more general, and have better sample complexity (empirically). Our experiments test PPO on a collection of benchmark tasks, including simulated robotic locomotion and Atari game playing, and we show that PPO outperforms other online policy gradient methods, and overall strikes a favorable balance between sample complexity, simplicity, and wall-time.

本研究提出了一种新的针对增强学习的策略梯度方法，称为近端策略优化(PPO)，通过与环境的交互采样数据，并使用随机梯度上升优化“替代”目标函数，不同于标准的策略梯度方法，该方法可以实现多个小批量更新周期，实验结果表明PPO在模拟机器人运动和Atari视频游戏玩耍等基准任务上的表现优于其他在线策略梯度方法，同时在样本复杂度、实现简单性和时间效率等方面取得了有利的平衡。

近端策略优化算法