nikhilbarhate99/PPO-PyTorch resource

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

2375 stars · 424 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A minimal, single-threaded PyTorch implementation of Proximal Policy Optimization (PPO) with clipped objective for OpenAI gym environments. It is designed primarily as an educational resource for beginners learning reinforcement learning, with training, testing, plotting, and GIF-making utilities.

Use cases

• learn how the PPO reinforcement learning algorithm works
• train a PPO agent on OpenAI gym environments
• understand a minimal policy gradient implementation in PyTorch
• test pretrained PPO policies and generate gifs of agent behavior
• plot training reward curves from csv logs
• run reinforcement learning experiments in Google Colab

When to choose

• you are a beginner wanting readable, minimal PPO code to study
• you need a simple baseline PPO implementation for standard gym environments
• you want a Colab notebook to experiment with PPO without local setup

When to avoid

• you need a production-grade, highly optimized PPO with parallel workers and GAE
• you require state-of-the-art PPO implementation details for complex environments
• you need a maintained library with an API rather than a reference codebase

Facets

learning-resource · maturity stable

machine-learning reinforcement-learning deep-learning reinforcement-learning machine-learning tutorials python cross-platform ppo pytorch policy-gradient openai-gym educational minimal-implementation

Member repositories

For agents

markdown · JSON · MCP: product_card(name="nikhilbarhate99/PPO-PyTorch")