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Modifiable OpenAI Gym environments for studying generalization in RL
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README.md

RL environments for evaluating generalization

This repo contains a set of environments (based on OpenAI Gym and Roboschool), designed for evaluating generalization in reinforcement learning. We also include implementations of several deep reinforcement learning algorithms (based on OpenAI Baselines), which we have evaluated on these environments.

All environments tested using Python 3.

Installation

Using virtualenv

We recommend that you install inside a virtualenv.

Install the environments by checking out this repository and running:

pip3 install --process-dependency-links -e .

Some examples of agents using the environments are provided in the examples directory. They require some additional dependencies, which can be installed by running:

pip3 install --process-dependency-links -e .[examples]

To get a list of all provided environments, you can run:

python3 -m examples.list_environments

Install Roboschool separately following the instructions here.

Using Docker

You can use Docker to avoid issues while installing dependencies such as Roboschool. You can clone the following Docker image which has all of the dependencies installed:

# download docker image
docker pull cpacker/rl-generalization

# start an interactive bash session
docker run -v /path/to/your/copy/of/rl-generalization:/rl-generalization -it cpacker/rl-generalization /bin/bash

# (inside container)
cd /rl-generalization	
python3 -m examples.list_environments

Using the modified environments

You can substitute our environments anywhere you would use an OpenAI Gym environment. For example, instead of:

import gym
env = gym.make('CartPole-v0')

You can use one of the following environments:

import gym
import sunblaze_envs

# Deterministic: the default version with fixed parameters
fixed_env = sunblaze_envs.make('SunblazeCartPole-v0')

# Random: parameters are sampled from a range nearby the default settings
random_env = sunblaze_envs.make('SunblazeCartPoleRandomNormal-v0')

# Extreme: parameters are sampled from an `extreme' range
extreme_env = sunblaze_envs.make('SunblazeCartPoleRandomExtreme-v0')

In the case of CartPole, RandomNormal and RandomExtreme will vary the strength of each actions, the mass of the pole, and the length of the pole:

CartPole D/R/E

Specific ranges for each environment setting are listed here. See the code in examples for usage with example algorithms from OpenAI Baselines.

Citations

To cite this repository in your research, you can reference the following paper:

Charles Packer, Katelyn Gao, Jernej Kos, Philipp Krähenbühl, Vladlen Koltun, and Dawn Song. Assessing Generalization in Deep Reinforcement Learning. arXiv preprint arXiv:1810.12282 (2018).

@misc{PackerGao:1810.12282,
  Author = {Charles Packer and Katelyn Gao and Jernej Kos and Philipp Kr\"ahenb\"uhl and Vladlen Koltun and Dawn Song},
  Title = {Assessing Generalization in Deep Reinforcement Learning},
  Year = {2018},
  Eprint = {arXiv:1810.12282},
}
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