nikhilbarhate99/PPO-PyTorch
Minimal implementation of clipped objective Proximal Policy Optimization (PPO) in PyTorch
This project helps reinforcement learning practitioners train AI agents for simulated environments like those found in OpenAI Gym. It takes environment observations and desired reward signals, then outputs a trained agent that can perform actions to maximize rewards, along with performance logs and visualizations. It is designed for researchers and students learning or implementing the Proximal Policy Optimization (PPO) algorithm.
2,320 stars. No commits in the last 6 months.
Use this if you are a researcher or student looking for a straightforward PyTorch implementation of the PPO algorithm to train AI agents in simulation environments.
Not ideal if you need a production-ready, highly optimized, or multi-threaded PPO implementation for complex, real-world control systems.
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Python
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MIT
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Jul 09, 2024
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