easyagents 1.1.13

reinforcement learning for practitioners.

Reinforcement Learning for Practitioners (v1.1, 19Q3)

Status: under active development, breaking changes may occur. Release notes.

EasyAgents is a high level reinforcement learning api, written in Python and running on top of OpenAI gym using algorithms implemented in tf-Agents, OpenAI baselines and huskarl.

Use EasyAgents if

• you have a gym environment and want to experiment with it
• you want try out different libraries and algorithms, but don't want to learn the details of each implementation
• you are looking for an easy and simple way to get started with reinforcement learning

Try it on colab:

• Cartpole on colab introduction: training, plotting & switching algorithms. based on the classic reinforcement learning example balancing a stick on a cart.
• Berater on colab example of a custom environment & training. gym environment based on a routing problem.
• LineWorld on colab implement your own environment, workshop example [work in progress]

In collaboration with Oliver Zeigermann.

Scenario: simple

from easyagents.agents import PpoAgent
from easyagents.callbacks import log, plot, duration

ppoAgent = PpoAgent('CartPole-v0')
ppoAgent.train([plot.State(), plot.Loss(), plot.Actions(), plot.Rewards()])

Scenario: more detailed (custom network & training)

from easyagents.agents import PpoAgent
from easyagents.callbacks import plot, duration

ppoAgent = PpoAgent( 'Orso-v1',fc_layers=(500,500,500))
ppoAgent.train([plot.State(),   plot.Loss(),        plot.Rewards(), 
                plot.Actions(), plot.StepRewards(), plot.Steps(), plot.ToMovie()], 
                learning_rate = 0.0001, num_iterations = 500,     max_steps_per_episode=50 )

Guiding Principles

• easily train, evaluate & debug policies for (you own) gym environment over "designing new algorithms"
• simple & consistent over "flexible & powerful"
• inspired by keras:
  • same api across all algorithms
  • support different implementations of the same algorithm
  • extensible (pluggable backends, plots & training schemes)

Installation

Install from pypi using pip:

pip install easyagents

Documentation

release notes, glossary, class diagrams

EasyAgents may not be ideal if

• you would like to leverage implementation specific advantages of an algorithm
• you want to do distributed or in parallel reinforcement learning

Note

• This repository is under active development and in an early stage. Thus any- and everything may (and probably should) change.
• If you have any difficulties in installing or using easyagents please let us know. We'll try to do our best to help you.
• Any ideas, help, suggestions, comments etc in python / open source development / reinforcement learning / whatever are more than welcome. Thanks a lot in advance.