Training a Resilient Q-Network against Observational Interference, Causal Inference Q-Networks

Last update: Nov 21, 2022

Overview

Obs-Causal-Q-Network

AAAI 2022 - Training a Resilient Q-Network against Observational Interference

Environment Setup

option 1 (from conda .yml under conda 10.2 and python 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 1 for 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 3 for 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}
}

Bayesian-Torch is a library of neural network layers and utilities extending the core of PyTorch to enable the user to perform stochastic variational inference in Bayesian deep neural networks

Bayesian-Torch is a library of neural network layers and utilities extending the core of PyTorch to enable the user to perform stochastic variational inference in Bayesian deep neural networks. Bayesian-Torch is designed to be flexible and seamless in extending a deterministic deep neural network architecture to corresponding Bayesian form by simply replacing the deterministic layers with Bayesian layers.

210 Jan 4, 2023

Defending graph neural networks against adversarial attacks (NeurIPS 2020)

Training a Resilient Q-Network against Observational Interference, Causal Inference Q-Networks

Related tags

Overview

Obs-Causal-Q-Network

Environment Setup

1. Example of Training Causal Inference Q-Network (CIQ) on Cartpole

2. Example of Training a "Variational" Causal Inference Q-Network on Unity 3D Banana Navigator

Reference

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