Proximal Policy Optimization Algorithms | alphaXiv
alphaXiv 0 CY V2 CLAIM AUTHORSHIP Proximal Policy Optimization Algorithms John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, Oleg Klimov OpenAI { joschu, filip, prafulla, alec, oleg } @openai.com Abstract We propose a new family of policy gradient methods for reinforcement learning, which al- ternate between sampling data through interaction with the environment, and optimizing a “surrogate” objective function using stochastic gradient ascent. Whereas standard policy gra- dient 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 optimiza- tion (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, includ- ing simulated robotic locom
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