OpenAI Baselines PPO - An official implementation of the Proximal Policy Optimization (PPO) algorithm by OpenAI.

## Definition of OpenAI Baselines PPO OpenAI Baselines PPO is the official implementation of the Proximal Policy Optimization (PPO) algorithm by OpenAI. PPO is a reinforcement learning algorithm that optimizes policies directly through a surrogate objective function, ensuring stable and efficient training. It supports both continuous and discrete action spaces and is widely used in robotics and gaming. ## Key Features of OpenAI Baselines PPO The key features of OpenAI Baselines PPO include: 1. **Clipped Objective Function**: Limits the magnitude of policy updates to prevent training instability. 2. **Action Space Support**: Supports both continuous and discrete action spaces, making it suitable for various reinforcement learning tasks. 3. **Wide Application**: Used in robotics control (e.g., Mujoco environments) and gaming (e.g., Atari games), such as training agents to perform well in Pong. ## Training Stability in OpenAI Baselines PPO OpenAI Baselines PPO ensures training stability by using a clipped objective function. This function limits the magnitude of policy updates, preventing the policy from deviating too much and causing training instability. The algorithm also employs an actor-critic framework, where the actor (policy network) selects actions, and the critic (value function network) estimates state values to help compute the advantage function for policy optimization. ## Installation and Local Usage of olmOCR OpenAI Baselines PPO supports environments provided by Gym, such as CartPole and Atari games, as well as custom environments. For example, users can train agents in Atari Pong using the command `python -m baselines.run --alg=ppo2 --env=PongNoFrameskip-v4` (40 million frames = 10 million timesteps) or in Mujoco Ant using `python -m baselines.run --alg=ppo2 --env=Ant-v2 --num_timesteps=1e6` (1 million timesteps). ## Definition of OpenAI Baselines PPO OpenAI Baselines PPO is the official implementation of the Proximal Policy Optimization (PPO) algorithm described in the 2017 paper "Proximal Policy Optimization Algorithms" by OpenAI. The implementation directly relates to the paper, incorporating the original algorithm's details, such as the clipped objective function and the actor-critic framework. It is designed for users to study and apply the PPO algorithm as described in the paper. ## Installation and Local Usage of olmOCR Examples of training commands for OpenAI Baselines PPO include: - **Atari Pong**: `python -m baselines.run --alg=ppo2 --env=PongNoFrameskip-v4` (40 million frames = 10 million timesteps) - **Mujoco Ant**: `python -m baselines.run --alg=ppo2 --env=Ant-v2 --num_timesteps=1e6` (1 million timesteps) These commands are used to train agents in specific environments, demonstrating the algorithm's application in gaming and robotics. ### Citation sources: - [OpenAI Baselines PPO](https://github.com/openai/baselines/tree/master/baselines/ppo2) - Official URL Updated: 2025-03-28