Jul 20, 2017 · 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 …

Proximal policy optimization (PPO) is a reinforcement learning (RL) algorithm for training an intelligent agent. Specifically, it is a policy gradient method , often used for deep RL when the policy network is very large.

I am currently a researcher at Anthropic, where I’m working on aligning large language models; some of my interests include scalable oversight and developing better written specifications of model behavior (like OpenAI’s Model Spec, Constitutional AI).

Our experiments test PPO on a collection of benchmark tasks, includ- ing 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. 1 Introduction.

Jul 20, 2017 · 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 …

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 …

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 …

Sep 4, 2023 · Introduced in 2017 by John Schulman et al., Proximal Policy Optimization (PPO) still stands out as a reliable and effective reinforcement learning algorithm. In this blog post, we’ll explore the fundamentals of PPO, its evolution from Trust Region Policy Optimization (TRPO), how it works, and its challenges.

May 6, 2021 · One of the core algorithms in this policy gradient/actor-critic field is Proximal Policy Optimization Algorithm implemented by OpenAI. In this post, I try to accomplish the following: We first need to understand the optimization objective of …

John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347, 2017.