saqib1707/RL-PPO-PyTorch
Simple and Modular implementation of Proximal Policy Optimization (PPO) in PyTorch
This tool helps machine learning practitioners or researchers quickly set up and experiment with a Proximal Policy Optimization (PPO) model for various control tasks. You provide a task environment, and the tool helps train an AI agent to perform actions within that environment, yielding a trained PPO model. It's designed for those exploring reinforcement learning.
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Use this if you are a machine learning student or researcher looking for a straightforward PyTorch implementation of the PPO algorithm to learn from and apply to standard simulation environments like CartPole or LunarLander.
Not ideal if you need a production-ready, highly optimized reinforcement learning solution for complex real-world control systems or if you are not comfortable with Python and machine learning development.
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Python
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MIT
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Last pushed
Oct 21, 2024
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