ivy-llc/gym
Fully differentiable RL environments, written in Ivy.
This tool helps machine learning researchers and developers explore new ways to train AI agents in simulated environments. It takes standard reinforcement learning tasks, like balancing a pole or controlling a swimmer, and transforms them into a format where the agent's performance can be directly optimized using supervised learning techniques. This allows for a more direct approach to improving AI behavior without needing traditional reinforcement learning methods.
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Use this if you are an AI researcher or developer working with reinforcement learning and want to experiment with directly optimizing cumulative rewards in simulated environments using differentiable methods.
Not ideal if you are looking for a simple, out-of-the-box reinforcement learning library for standard agent training without exploring novel differentiable optimization techniques.
Stars
66
Forks
9
Language
Python
License
Apache-2.0
Category
Last pushed
Aug 28, 2023
Commits (30d)
0
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