Collaborative Deep Reinforcement Learning
Collaborative Deep Reinforcement Learning (CDRL) is a collaborative framework to enable knowledge transfer among heterogeneous tasks.
Dependencies
- Python 2.7
- six (for py2/3 compatibility)
- TensorFlow 0.11
- gym
- gym[atari]
- universe
- opencv-python
- numpy
- scipy
Folder structure
|>CDRL
The code for experiments in paper section 5.4 Collaborative Deep Reinforcement Learning
|>heterogeneousTransfer
The code for experiments in paper section 5.3 Certificated Heterogeneous Transfer
|>model
The pre-trained model used for section heterogeneous transfer
How to run:
|>heterogeneousTransfer
python train.py --num-MTL-workers 8 0 --env-id PongDeterministic-v3_Bowling-v0
|>CDRL
python train.py --num-MTL-workers 8 8 --env-id PongDeterministic-v3_Bowling-v0
Preprint:
Collaborative Deep Reinforcement Learning
Kaixiang Lin, Shu Wang and Jiayu Zhou
Acknowledgement
This code refers to OpenAI universe starter agent.