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jaco_arm
.gitignore
LICENSE
README.md
main.py
model.py
optim.py
random_agent.py
test.py
train.py
utils.py

README.md

a3c-mujoco

MIT License

Simulated target reaching tasks using the MuJoCo physics engine. The setup is adapted from [1] end-to-end learning setup for solving pixel-driven control of Jaco arm where learning is accomplished using Asynchronous Advantage Actor-Critic (A3C)[2] method with sparse rewards.

Usage:

Run with python main.py <options>.

Dependencies:

Note:

Obtain a 30-day free trial on the MuJoCo website or free license if you are a student.

Results

Acknowledgements

References