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Modified CartPole-v0 OpenAI Gym environment with various noisy cases and Reinforcement Learning based controller

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This repository contains a PIP package which is a modified version of the CartPole-v0 OpenAI environment which includes cart & pole friction and random sensor & actuator noise.

Installation

Install OpenAI gym.

Then install this package via

git clone http://github.com/AadityaPatanjali/gym-cartpolemod.git
cd gym-cartpolemod
sudo pip install -e .

Usage

Python usage:

import gym
import gym_cartpolemod

env = gym.make('CartPoleMod-v0')

Examples: Versions go from v0 through v6 for different noise scenarios

cd example_files
python deepQNetwork.py v1

The Environment

Some parameters for the cart-pole system:

  • mass of the cart = 1.0
  • mass of the pole = 0.1
  • length of the pole = 0.5
  • magnitude of the force = 10.0
  • friction at the cart = 5e-4
  • friction at the pole = 2e-6

Noise cases(v0 to v6):

  • v0: Noise free
  • v1: 5% Uniform Actuator noise
  • v2: 10% Uniform Actuator noise
  • v3: 5% Uniform Sensor noise
  • v4: 10% Uniform Sensor noise
  • v5: 0.1 var Gaussian Sensor noise
  • v6: 0.2 var Gaussian Sensor noise

Note: The sensor noise is added to the angle, theta alone, and the actuator noise is added to the force.

Some Neural network parameters:

  • Discount rate : gamma = 0.95
  • Exploration rate : epsilon = 1.0
  • Exploration decay : 90%
  • Learning rate : 0.01
  • NN Input layer : 24 neurons, tanh activation function
  • NN Hidden layer : 48 neurons, tanh activation function
  • NN Output layer : 2 neurons, linear activation function
  • MSE loss function with Adam Optimizer
  • Seed : None (Both in deepQNetwork and cartpolemod_env)

Note: To give a seed, uncomment line# 17 in deepQNetwork and change line# 76 in cartpole_mod to the desired seed value.

Notations:

  • 1 run : 1000 trials
  • 1 trials : 60,000 time-steps
  • 1 time step : 0.02s
  • Success rate : successful runs for 100 runs
  • Successful trial: System lasts 60k time steps without failing
  • Successful run : Atleast one successful trial in 1000 trials
  • Average trials : Mean trials to successful trial over 100 runs
  • Execution time : Approximate average time of execution for 100 runs

Results

CartpoleMod Version Noise type Success rate Average trials Execution time
v0 Noise free 100% 119.68 ~ 4.5 hours
v1 5% Uniform Actuator Noise 100% 118.88 ~ 4.5 hours
v2 10% Uniform Actuator Noise 100% 130.47 ~ 4.5 hours
v3 5% Uniform Sensor Noise 100% 123.93 ~ 5.5 hours
v4 10% Uniform Sensor Noise 100% 133.67 ~ 5.5 hours
v5 σ^2 = 0.1 Gaussian Sensor Noise 100% 179.36 ~ 6.5 hours
v6 σ^2 = 0.2 Gaussian Sensor Noise 98% 209.59 ~ 8 hours

The team

  • Aaditya Ravindran
  • Apoorva Sonavani
  • Rohith Krishna Gopi

This was created as a part of Prof. Jennie Si's class on Artificial Neural Computation (Fall 2017) at Arizona State University

Special thanks to MartinThoma, Kevin Frans, and keon

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Modified CartPole-v0 OpenAI Gym environment with various noisy cases and Reinforcement Learning based controller

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