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Stephanie Hughes edited this page Jun 22, 2021
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This is an implementation of Deep Deterministic Policy Gradient from Demonstration (DDPGfD) to train a policy to perform "near-contact" grasping tasks, where object's starting position is random within graspable region. We took one "near-contact" strategy from this paper as expert demonstration and train a RL controller to handle a variety of objects with random starting position.
This environment runs on MuJoCo with an intergration of OpenAI gym to facilitate the data collection and traning process.
- Getting Started
- Software Setup
- How to run the code and experiments
- Data and input variations
- Reinforcement Learning structure
- PID Controllers
- Policy learning
- Evaluation
- Software development structure
- Common issues + fixes