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.
New policy gradient methods called Proximal Policy Optimization (PPO) are introduced, enhancing the efficiency of reinforcement learning.
PPO simplifies implementation, is general in application, and shows improved sample efficiency.
It addresses issues with Q-Learning, vanilla policy gradient methods, and Trust Region Policy Optimization (TRPO).
Introduces a clipped surrogate objective function to reduce large policy updates and improve stability.
Empirical results show PPO outperforms other algorithms in various benchmarks, with easy adoption for existing methods.
In "Proximal Policy Optimization Algorithms," the authors present a new set of policy gradient methods that aim to enhance the efficiency of reinforcement learning. These methods, collectively termed Proximal Policy Optimization (PPO), aim to balance simplicity in implementation, generality, and improved sample efficiency.
PPO operates with a novel approach to addressing the shortcomings of existing methods, such as Q-Learning and Trust Region Policy Optimization (TRPO). The paper asserts that while Q-Learning with function approximation may fail on simple tasks and is not well-understood, vanilla policy gradient methods exhibit poor data efficiency. TRPO, although reliable, is criticized for complexity and incompatibility with certain neural network architectures.
This work introduces an alternative to the "surrogate" objective function used in TRPO. The authors advocate for optimizing a modified objective with clipped probability ratios to create a pessimistic estimate of policy performance and circumvent excessively large policy updates. A balance between clipped and unclipped objectives is proposed, favoring the former because it acts as a lower bound of the surrogate, mitigating policy deviation and advocating stability.
Their empirical evaluation indicates that PPO outperforms a range of well-established algorithms in continuous control and Atari benchmarks. Specifically, for continuous control tasks, PPO is demonstrated to be superior both in terms of sample efficiency and simplicity relative to A2C and similarly to ACER. PPO leverages multiple epochs of mini-batch updates, which the authors suggest is more practical than the single-step updates in TRPO.
In conclusion, the paper signals PPO as a significant stride towards creating more reliable yet simpler to execute policy-based methods for reinforcement learning, emphasizing versatility through less hyperparameter tuning. The methods' success in generalizing across a variety of tasks, and their impressive empirical performance, solidify PPO's position as a leading technique in reinforcement learning. Moreover, due to their accessible nature—for most implementations require minimal modification of existing Policy Gradient methods—PPO algorithms stand poised for widespread adoption and continual improvement.