rmst/ddpg
TensorFlow implementation of the DDPG algorithm from the paper Continuous Control with Deep Reinforcement Learning (ICLR 2016)
This project helps machine learning researchers or practitioners develop AI agents that can learn complex motor skills or decision-making processes through trial and error. It takes in descriptions of environments where an agent needs to learn to act, such as simulated robots or game scenarios, and outputs a trained AI policy that can continuously control actions to achieve a goal. It is used by those experimenting with deep reinforcement learning for continuous control tasks.
215 stars. No commits in the last 6 months.
Use this if you are a machine learning researcher or engineer looking to experiment with the Deep Deterministic Policy Gradient (DDPG) algorithm for continuous control problems.
Not ideal if you are looking for a maintained, state-of-the-art DDPG implementation with advanced features like batch normalization or prioritized experience replay.
Stars
215
Forks
64
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Feb 16, 2018
Commits (30d)
0
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