Code, environments, and scripts for the paper: "How Private Is Your RL Policy? An Inverse RL Based Analysis Framework"

Related tags

Deep LearningPRIL
Overview

Privacy-Aware Inverse RL (PRIL) Analysis Framework

Code, environments, and scripts for the paper: "How Private Is Your RL Policy? An Inverse RL Based Analysis Framework" - AAAI 2022
Link to paper: _____

Setup and installation instructions

All experiments performed are performed within a conda environment with python=3.8 We use the following external libraries:

  • numpy
  • pandas
  • TensorFlow v2.5.0
  • TensorFlow Privacy v0.5.2
  • PyTorch v1.9.0
  • gym
  • cvxopt
  • plotly

How to run experiments

1. DQN

python dqn.py

Arguments

  • --with_privacy : if set to true, the code will run private DQN
  • --type : optimizer type (either SGD or Adam)
  • --activation : activation function of model (either relu or shoe)
  • --env_size : grid size of FrozenLake environment (either 5x5 or 10x10)
  • --with_testing : if set to true, the code will run tests too

2. PPO

python ppo.py

Arguments

  • --with_privacy : if set to true, the code will run private PPO
  • --type : optimizer type (either SGD or Adam)
  • --activation : activation function of model (either relu or shoe)
  • --env_size : grid size of FrozenLake environment (either 5x5 or 10x10)
  • --with_testing : if set to true, the code will run tests too

3. VI

python vi.py

Arguments

  • --with_privacy : if set to true, the code will run private value iteration
  • --env_size : grid size of FrozenLake environment (either 5x5 or 10x10)
  • --with_testing : if set to true, the code will run tests too

4. DQNFN

python dqnfn.py

Arguments

  • --with_privacy : if set to true, the code will run private DQNFN
  • --env_size : grid size of FrozenLake environment (either 5x5 or 10x10)
  • --with_testing : if set to true, the code will run tests too
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