This is the repository of Robotic Arm Motion Planning Simulation Based on Reinforcement Learning.
In this code, I use a self-built pybullet robot arm reinforcement learning environment to test some reinforcement learning algorithms, including DDPG
, TD3
, DADDPG
, DATD3
, and DARC
, and try to let the robot arm finish three tasks: reach, push, and pick.
I use main.py
to run results, the algorithms' parameters are in config.py
, and use visdom
to monitor the algorithms' performance. The envs
deposits three self-built robot arm environments, the algo
deposits test algorithms, the models
deposits robot urdf
file, and the utils
deposits small tools for rl-learning.
python -m visdom.server
Run the following commands to start visdom server.
python main.py run --env=<envrionment name> --algo=<algorithm name> --vis_name=<visdom server name>
I use fire
console to run my code, so use the following commands to run the simulation, you also can change the config in config.py
instead.
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