Skip to main content

Reinforcement learning benchmark problems set in dynamic environments.

Project description

Dynamic reinforcement learning benchmarks

This repository contains three open source reinforcement learning environments that require the agent to adapt its behavior to or make use of dynamic elements in the environment in order to solve the task. The environments follow OpenAI's gym interface.

A picture of the included environments

Installation

With python3.7 or higher run

pip install dyn_rl_benchmarks

Usage

After importing the package dyn_rl_benchmarks the environments

  • Platforms-v1
  • Drawbridge-v1
  • Tennis2D-v1

are registered and can be instantiated via gym.make.

The following example runs Platforms-v1 with randomly sampled actions:

import gym

import dyn_rl_benchmarks

env = gym.make("Platforms-v1")

obs = env.reset()
done = False
while not done:
  action = env.action_space.sample()
  obs, rew, done, info = env.step(action)
  env.render()

How to cite

@article{gurtler2021hierarchical,
  title={Hierarchical Reinforcement Learning with Timed Subgoals},
  author={G{\"u}rtler, Nico and B{\"u}chler, Dieter and Martius, Georg},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  year={2021}
}

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

dyn_rl_benchmarks-1.0.3.tar.gz (35.0 kB view hashes)

Uploaded Source

Built Distribution

dyn_rl_benchmarks-1.0.3-py3-none-any.whl (38.4 kB view hashes)

Uploaded Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page