Reinforcement Learning
Training and Playing
Train an agent with RSL-RL:
- dVRK-PSM Reach (
Isaac-Reach-PSM-v0):
# run script for training
python workflows/robotic_surgery/scripts/simulation/scripts/reinforcement_learning/rsl_rl/train.py --task Isaac-Reach-PSM-v0 --headless
# run script for playing with 50 environments
python workflows/robotic_surgery/scripts/simulation/scripts/reinforcement_learning/rsl_rl/play.py --task Isaac-Reach-PSM-Play-v0
- Suture Needle Lift (
Isaac-Lift-Needle-PSM-IK-Rel-v0):
# run script for training
python workflows/robotic_surgery/scripts/simulation/scripts/reinforcement_learning/rsl_rl/train.py --task Isaac-Lift-Needle-PSM-IK-Rel-v0 --headless
# run script for playing with 50 environments
python workflows/robotic_surgery/scripts/simulation/scripts/reinforcement_learning/rsl_rl/play.py --task Isaac-Lift-Needle-PSM-IK-Rel-Play-v0
TensorBoard: TensorFlow's visualization toolkit
Monitor the training progress stored in the logs directory on Tensorboard:
# execute from the root directory of the repository
python -m tensorboard.main --logdir=logs