What are some ways to render a DDPG autonomous quadcopter like openai gym?
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I completed the Udacity nanodegree in Deep Learning but found the final project to be extremely difficult since the only visual feedback were from plots via matplotlib (rotor speeds, x/y/z coordinates, x/y/z velocity, etc). Is there any way to render the learning per episode as done in openai gym?
A quick search for Python game engines only confused me more in terms of which one to use.
Here is a link to the final project, ie. fly a quadcopter using DDPG algorithm: https://github.com/udacity/RL-Quadcopter-2
It has a physics_sim.py, task.py, agent.py and a main ipynb notebook.
visualization reinforcement-learning
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I completed the Udacity nanodegree in Deep Learning but found the final project to be extremely difficult since the only visual feedback were from plots via matplotlib (rotor speeds, x/y/z coordinates, x/y/z velocity, etc). Is there any way to render the learning per episode as done in openai gym?
A quick search for Python game engines only confused me more in terms of which one to use.
Here is a link to the final project, ie. fly a quadcopter using DDPG algorithm: https://github.com/udacity/RL-Quadcopter-2
It has a physics_sim.py, task.py, agent.py and a main ipynb notebook.
visualization reinforcement-learning
asked yesterday
flexitarian33flexitarian33
256
New contributor
flexitarian33 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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add a comment |
$begingroup$
I completed the Udacity nanodegree in Deep Learning but found the final project to be extremely difficult since the only visual feedback were from plots via matplotlib (rotor speeds, x/y/z coordinates, x/y/z velocity, etc). Is there any way to render the learning per episode as done in openai gym?
A quick search for Python game engines only confused me more in terms of which one to use.
Here is a link to the final project, ie. fly a quadcopter using DDPG algorithm: https://github.com/udacity/RL-Quadcopter-2
It has a physics_sim.py, task.py, agent.py and a main ipynb notebook.
visualization reinforcement-learning
asked yesterday
flexitarian33flexitarian33
256
New contributor
flexitarian33 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I completed the Udacity nanodegree in Deep Learning but found the final project to be extremely difficult since the only visual feedback were from plots via matplotlib (rotor speeds, x/y/z coordinates, x/y/z velocity, etc). Is there any way to render the learning per episode as done in openai gym?
A quick search for Python game engines only confused me more in terms of which one to use.
Here is a link to the final project, ie. fly a quadcopter using DDPG algorithm: https://github.com/udacity/RL-Quadcopter-2
It has a physics_sim.py, task.py, agent.py and a main ipynb notebook.
visualization reinforcement-learning
visualization reinforcement-learning
asked yesterday
flexitarian33flexitarian33
256
New contributor
flexitarian33 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked yesterday
flexitarian33flexitarian33
256
New contributor
flexitarian33 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked yesterday
flexitarian33flexitarian33
256
New contributor
flexitarian33 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked yesterday
flexitarian33flexitarian33
256
asked yesterday
flexitarian33flexitarian33
256
256
New contributor
flexitarian33 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
flexitarian33 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
flexitarian33 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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