BY571/CQL
PyTorch implementation of the Offline Reinforcement Learning algorithm CQL. Includes the versions DQN-CQL and SAC-CQL for discrete and continuous action spaces.
This project helps machine learning researchers and practitioners implement and experiment with Conservative Q-Learning (CQL) for offline reinforcement learning. It takes pre-recorded datasets of environment interactions and uses them to train AI agents that can make decisions without further real-world interaction. The output is a trained agent capable of performing tasks based on the learned policies.
148 stars. No commits in the last 6 months.
Use this if you are a researcher or AI engineer working with reinforcement learning and need to train agents robustly from existing, static datasets without needing live environment interaction.
Not ideal if you are looking for a simple, out-of-the-box solution to deploy a reinforcement learning agent in a live environment without any prior experience in offline RL.
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148
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24
Language
Python
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Last pushed
May 06, 2024
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