tensorlayer/RLzoo
A Comprehensive Reinforcement Learning Zoo for Simple Usage 🚀
This collection helps researchers and practitioners quickly implement and test various reinforcement learning algorithms. You provide the problem environment (like a robotic simulation or game) and the system outputs a trained "agent" that can learn to make optimal decisions. It's designed for anyone looking to experiment with and apply reinforcement learning to solve complex control and decision-making tasks.
644 stars. No commits in the last 6 months. Available on PyPI.
Use this if you need a high-level, easy-to-use framework to apply and benchmark established reinforcement learning algorithms in simulated environments.
Not ideal if you require low-level control over the reinforcement learning algorithm's internal mechanics or are developing entirely new algorithms from scratch.
Stars
644
Forks
97
Language
Python
License
Apache-2.0
Category
Last pushed
Mar 24, 2023
Commits (30d)
0
Dependencies
9
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