Benchmark for Robustness Tests of Control Alrogithms

Overview

Benchmark for Robustness Tests of Control Alrogithms

This repository contains classical control benchmarks for evaluating robustnesses of control and reinforcement learning algorithms. It can be used as zero-shot control performence evaluations. It is built upon OpenAI Gym.

Installation

Clone repository, then 'pip install -e .' or 'pip3 install -e .' based on your environment.

Or you can manually install dependencies:

- numpy
- gym

How to Run Example

You can run our test example by:

python unstable_pendulum.py

It's a inverted pendulum in gym environment. The sample results of the four different controllers are shown below:

Sine wave side wind Random side wind

How to Use

Simply import environments from 'unstable_gym'. For examples, for inverted pendulum:

from unstable_gym.unstable_pendulum import UnstablePendulumEnv
env = UnstablePendulumEnv(wind_type="sine", max_wind=1.0)

obs = env.reset()
for step in range(500):
    action = env.action_space.sample()
    nobs, reward, done, info = env.step(action)
    env.render()

There are two options for "wind type":

  1. "sine" : sine wave side wind
  2. "random" : random side wind

You can also adjust the magnitude of the side wind (in [N]): "max_wind"

Related Works

You can test the robustness of MPPI and Smooth_MPPI

Owner
Kim Taekyung
Autonomous driving, robotics researcher @ ADD; B.S. @ DGIST; 1st winner in Samsung Robot Hackathon 2018,2019.
Kim Taekyung
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