pat-coady/trpo
Trust Region Policy Optimization with TensorFlow and OpenAI Gym
This project helps researchers and engineers quickly train robotic agents to perform complex movements and tasks in simulated environments. It takes a simulated robot environment, like a walking humanoid or a robotic arm, and outputs a trained 'brain' that allows the robot to learn how to achieve its goals without extensive manual tuning. It's designed for AI researchers, robotics engineers, and students exploring reinforcement learning for control problems.
361 stars. No commits in the last 6 months.
Use this if you need to efficiently train a variety of robotic agents in simulation, from simple pendulums to complex humanoids, using a robust, automatically tuned algorithm.
Not ideal if you are looking for a tool to control physical robots directly or if your primary focus is on very simple, non-continuous control problems.
Stars
361
Forks
107
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Jun 02, 2020
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