Obs-Causal-Q-Network
AAAI 2022 - Training a Resilient Q-Network against Observational Interference
Preprint | Slides | Colab Demo |
Environment Setup
- option 1 (from conda .yml under
conda 10.2andpython 3.6)
conda env create -f obs-causal-q-conda.yml
- option 2 (from a clean python 3.6 and please follow the setup of UnityAgent 3D environment for Banana Navigator )
pip install torch torchvision torchaudio
pip install dowhy
pip install gym
1. Example of Training Causal Inference Q-Network (CIQ) on Cartpole
- Run Causal Inference Q-Network Training (
--network 1for Treatment Inference Q-network)
python 0-cartpole-main.py --network 1
- Causal Inference Q-Network Architecture
- Output Logs
observation space: Box(4,)
action space: Discrete(2)
Timing Atk Ratio: 10%
Using CEQNetwork_1. Number of Params: 41872
Interference Type: 1 Use baseline: 0 use CGM: 1
With: 10.42 % timing attack
Episode 0 Score: 48.00, Average Score: 48.00, Loss: 1.71
With: 0.0 % timing attack
Episode 20 Score: 15.00, Average Score: 18.71, Loss: 30.56
With: 3.57 % timing attack
Episode 40 Score: 28.00, Average Score: 19.83, Loss: 36.36
With: 8.5 % timing attack
Episode 60 Score: 200.00, Average Score: 43.65, Loss: 263.29
With: 9.0 % timing attack
Episode 80 Score: 200.00, Average Score: 103.53, Loss: 116.35
Using CEQNetwork_1. Number of Params: 41872
### Evaluation Phase & Report DQNs Test Score : 193.4
Using CEQNetwork_1. Number of Params: 41872
### Evaluation Phase & Report DQNs Test Score : 164.2
Using CEQNetwork_1. Number of Params: 41872
### Evaluation Phase & Report DQNs Test Score : 147.8
Using CEQNetwork_1. Number of Params: 41872
### Evaluation Phase & Report DQNs Test Score : 193.4
With: 9.5 % timing attack
Episode 100 Score: 200.00, Average Score: 163.20, Loss: 77.38
Using CEQNetwork_1. Number of Params: 41872
### Evaluation Phase & Report DQNs Test Score : 198.4
Using CEQNetwork_1. Number of Params: 41872
### Evaluation Phase & Report DQNs Test Score : 200.0
Using CEQNetwork_1. Number of Params: 41872
### Evaluation Phase & Report DQNs Test Score : 200.0
Using CEQNetwork_1. Number of Params: 41872
### Evaluation Phase & Report DQNs Test Score : 197.8
Using CEQNetwork_1. Number of Params: 41872
### Evaluation Phase & Report DQNs Test Score : 200.0
Using CEQNetwork_1. Number of Params: 41872
### Evaluation Phase & Report DQNs Test Score : 200.0
Using CEQNetwork_1. Number of Params: 41872
### Evaluation Phase & Report DQNs Test Score : 200.0
Using CEQNetwork_1. Number of Params: 41872
### Evaluation Phase & Report DQNs Test Score : 197.6
Using CEQNetwork_1. Number of Params: 41872
### Evaluation Phase & Report DQNs Test Score : 198.6
Using CEQNetwork_1. Number of Params: 41872
### Evaluation Phase & Report DQNs Test Score : 200.0
Using CEQNetwork_1. Number of Params: 41872
### Evaluation Phase & Report DQNs Test Score : 200.0
Using CEQNetwork_1. Number of Params: 41872
### Evaluation Phase & Report DQNs Test Score : 199.0
Using CEQNetwork_1. Number of Params: 41872
### Evaluation Phase & Report DQNs Test Score : 200.0
Using CEQNetwork_1. Number of Params: 41872
### Evaluation Phase & Report DQNs Test Score : 186.8
Using CEQNetwork_1. Number of Params: 41872
### Evaluation Phase & Report DQNs Test Score : 200.0
Environment solved in 114 episodes! Average Score: 195.55
Environment solved in 114 episodes! Average Score: 195.55 +- 25.07
############# Basic Evaluate #############
Using CEQNetwork_1. Number of Params: 41872
Evaluate Score : 200.0
############# Noise Evaluate #############
Using CEQNetwork_1. Number of Params: 41872
Robust Score : 200.0
2. Example of Training a "Variational" Causal Inference Q-Network on Unity 3D Banana Navigator
- Run Variational Causal Inference Q-Networks (VCIQs) Training (
--network 3for Causal Variational Inference)
python 1-banana-navigator-main.py --network 3
- Variational Causal Inference Q-Network Architecture
- Output Logs
'Academy' started successfully!
Unity Academy name: Academy
Number of Brains: 1
Number of External Brains : 1
Lesson number : 0
Reset Parameters :
Unity brain name: BananaBrain
Number of Visual Observations (per agent): 0
Vector Observation space type: continuous
Vector Observation space size (per agent): 37
Number of stacked Vector Observation: 1
Vector Action space type: discrete
Vector Action space size (per agent): 4
Vector Action descriptions: , , ,
Timing Atk Ratio: 10%
Using CEVAE_QNetwork.
Unity Worker id: 10 T: 1 Use baseline: 0 CEVAE: 1
With: 9.67 % timing attack
Episode 0 Score: 0.00, Average Score: 0.00
With: 11.0 % timing attack
Episode 5 Score: 1.00, Average Score: 0.17
With: 11.33 % timing attack
Episode 10 Score: 0.00, Average Score: 0.36
With: 10.33 % timing attack
Episode 15 Score: 0.00, Average Score: 0.56
...
Episode 205 Score: 10.00, Average Score: 9.25
With: 9.33 % timing attack
Episode 210 Score: 9.00, Average Score: 9.70
With: 9.0 % timing attack
Episode 215 Score: 10.00, Average Score: 11.10
With: 8.33 % timing attack
Episode 220 Score: 14.00, Average Score: 10.85
With: 12.33 % timing attack
Episode 225 Score: 19.00, Average Score: 11.70
With: 11.0 % timing attack
Episode 230 Score: 18.00, Average Score: 12.10
With: 7.67 % timing attack
Episode 235 Score: 21.00, Average Score: 11.60
With: 9.67 % timing attack
Episode 240 Score: 16.00, Average Score: 12.05
Environment solved in 242 episodes! Average Score: 12.50
Environment solved in 242 episodes! Average Score: 12.50 +- 4.87
############# Basic Evaluate #############
Using CEVAE_QNetwork.
Evaluate Score : 12.6
############# Noise Evaluate #############
Using CEVAE_QNetwork.
Robust Score : 12.5
Reference
This fun work was initialzed when Danny and I first read the Causal Variational Model between 2018 to 2019 with the helps from Dr. Yi Ouyang and Dr. Pin-Yu Chen.
Please consider to reference the paper if you find this work helpful or relative to your research.
- A non-archival and preliminary venue was presented in ICLR 2021 Self-supervision for Reinforcement Learning Workshop, spotlight, long contributed talk. We appreciate the research community and the orgainzers.
@article{yang2021causal,
title={Causal Inference Q-Network: Toward Resilient Reinforcement Learning},
author={Yang, Chao-Han Huck and Hung, I and Danny, Te and Ouyang, Yi and Chen, Pin-Yu},
journal={arXiv preprint arXiv:2102.09677},
year={2021}
}
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