OpenAI is an AI development and deployment company based in San Francisco, California. It supports teaching agents everything from walking to playing games like Pong.

Our mission is to ensure that artificial general intelligence benefits all of humanity. Research Papers. January 23, 2020 Scaling Laws for Neural Language Models. 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.

OpenAI works on advancing AI capabilities, safety, and policy. Pendulum-v0 The inverted pendulum swingup problem is a classic problem in the control literature. The Our mission is to ensure that artificial general intelligence benefits all of humanity. Home; Environments; Documentation; Close. Progress. or Pinball.

It started a trail of research which ultimately led to stronger algorithms such as TRPO and then PPO soon after.

Milestone Releases. View documentation; View on GitHub; RandomAgent on CartPole-v1 RandomAgent on Pendulum-v0 RandomAgent on SpaceInvaders-v0 RandomAgent on LunarLander … Gym is a toolkit for developing and comparing reinforcement learning algorithms. Gym Gym is a toolkit for developing and comparing reinforcement learning algorithms.

PPO-Penalty approximately solves a KL-constrained update like TRPO, but penalizes the KL-divergence in the objective function instead of making it a hard constraint, and automatically adjusts the penalty coefficient over the course of training so that it’s scaled appropriately.

PPO2¶ The Proximal Policy Optimization algorithm combines ideas from A2C (having multiple workers) and TRPO (it uses a trust region to improve the actor).

The gym library is a collection of test problems — environments — that you can use to work out your reinforcement learning algorithms. In this version of the problem, the pendulum starts in a random position, and the … PPO strikes a balance between ease of implementation, sample complexity, and ease of tuning, trying to compute an update at each step that minimizes the cost function while ensuring the deviation from the previous policy is relatively small.

The main idea is that after an update, the new policy should be not too far from the old policy. It makes no assumptions about the structure of your agent, and is compatible with any numerical computation library, such as TensorFlow or Theano. Releases Papers. For that, PPO uses clipping to avoid too large update. OpenAI Gym. Nav. A key feature of this line of work is that all of these algorithms are on-policy: that is, they don’t use old data, which makes them weaker on sample efficiency.