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Reinforcement Learning agent using Deep Deterministic Policy Gradients (DDPG).
This reinforcement lerning model is a modified version of Udacity's DDPG model which is based on the paper Continuous control with deep reinforcement learning. This project was developed as part of the Machine Learning Engineer Nanodegree quadcopter project and the model is based on code provided in the project assignment.
Solving OpenAI Gym's MountainCarContinuous-v0 continuous control problem with this model provides a particularly good learning example as its 2-dimensional continuous state space (position and velocity) and 1-dimensional continuous action space (forward, backward) are easy to visualize in two dimensions, lending to an intuitive understanding of hyperparameter tuning.
Project development began as a kaggle kernel. Initial code in this repo is based on DDPG_OpenAI-MountainCarContinuous-V0 Version 74.
See Solving MountainCarContinuous-v0.ipynb
for an example of usage and a demo training visualization output.
FAQs
Reinforcement Learning model using Deep Deterministic Policy Gradients (DDPG)
We found that ddpg-agent demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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