UO
UoA-CARES/NaSA_TD3
Official PyTorch Implementation of Our Paper Image-Based Deep Reinforcement Learning with Intrinsically Motivated Stimuli: On the Execution of Complex Robotic Tasks
NaSA_TD3
Official PyTorch Implementation of Image-Based Deep Reinforcement Learning with Intrinsically Motivated Stimuli: On the Execution of Complex Robotic Tasks
General Overview
See our Paper-Blog for details for pseudocode with more details of the training process as well as details of hyperparameters, full source code and videos of each task.
Prerequisites
| Library | Version |
|---|---|
| Our RL Support Libray | link |
| DeepMind Control Suite | link |
Network Architecture
Instructions Training
To train the NaSA-TD3 algorithm on the deep mind control suite from image-based observations, please run:
python3 train_loop.py --env=ball_in_cup --task=catch --seed=1 --intrinsic=True
Our Results
Citation
If you use either the paper or code in your paper or project, please kindly star this repo and cite our work.