Project details for RLLib Lightweight On or Off Policy Reinforcement Learning Library

Logo RLLib Lightweight On or Off Policy Reinforcement Learning Library 2.0

by saminda - April 25, 2014, 02:58:32 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ]

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Description:

RLLib

(C++ Template Library to Predict, Control, Learn Behaviors, and Represent Learnable Knowledge using On/Off Policy Reinforcement Learning)

RLLib is a lightweight C++ template library that implements incremental, standard, and gradient temporal-difference learning algorithms in Reinforcement Learning. It is an optimized library for robotic applications and embedded devices that operates under fast duty cycles (e.g., < 30 ms). RLLib has been tested and evaluated on RoboCup 3D soccer simulation agents, physical NAO V4 humanoid robots, and Tiva C series launchpad microcontrollers to predict, control, learn behaviors, and represent learnable knowledge. The implementation of the RLLib library is inspired by the RLPark API, which is a library of temporal-difference learning algorithms written in Java.

Features

  • Off-policy prediction algorithms:
  • GTD(lambda)
  • GQ(lambda)
  • Off-policy control algorithms:
  • Greedy-GQ(lambda)
  • Softmax-GQ(lambda)
  • Off-PAC (can be used in on-policy setting)
  • On-policy algorithms:
  • TD(lambda)
  • TD(lambda)AlphaBound
  • TD(lambda)True
  • Sarsa(lambda)
  • Sarsa(lambda)AlphaBound
  • Sarsa(lambda)True
  • Sarsa(lambda)Expected
  • Actor-Critic (continuous actions, discrete actions, discounted reward settting, averaged reward settings, and so on)
  • Supervised learning algorithms:
  • Adaline
  • IDBD
  • KI
  • SemiLinearIDBD
  • Autostep
  • Policies: Random RandomX%Bias Greedy Epsilon-greedy Boltzmann Normal Softmax
  • Dot product: An efficient implementation of the dot product for tile coding based feature representations (with culling traces).
  • Benchmarking environments: Mountain Car Mountain Car 3D Swinging Pendulum Continuous Grid World Bicycle Cart Pole Acrobot Non-Markov Pole Balancing Helicopter
  • Optimization: Optimized for very fast duty cycles (e.g., with culling traces, RLLib has been tested on the Robocup 3D simulator agent, and on the NAO V4 (cognition thread)).
  • Usage: The algorithm usage is very much similar to RLPark, therefore, swift learning curve.
  • Examples: There are a plethora of examples demonstrating on-policy and off-policy control experiments.
  • Visualization: We provide a Qt4 based application to visualize benchmark problems.

Extension

Extension for Tiva C Series EK-TM4C123GXL LaunchPad, and Tiva C Series TM4C129 Connected LaunchPad microcontrollers.

Tiva C series launchpad microcontrollers: https://github.com/samindaa/csc688

Demo

Off-PAC ContinuousGridworld AverageRewardActorCritic SwingPendulum (Continuous Actions)

Usage

RLLib is a C++ template library. The header files are located in the src directly. You can simply include this directory from your projects, e.g., -I./src, to access the algorithms.

To access the control algorithms:

#include "ControlAlgorithm.h"

To access the predication algorithms:

#include "PredictorAlgorithm"

To access the supervised learning algorithms:

#include "SupervisedAlgorithm.h"

RLLib uses the namespace:

using namespace RLLib

Testing

RLLib provides a flexible testing framework. Follow these steps to quickly write a test case.

  • To access the testing framework: #include "HeaderTest.h"

`javascript

include "HeaderTest.h"

RLLIB_TEST(YourTest)

class YourTest Test: public YourTestBase { public: YourTestTest() {}

virtual ~Test() {}
void run();

private: void testYourMethod(); };

void YourTestBase::testYourMethod() {/* Your test code /}

void YourTestBase::run() { testYourMethod(); } `

  • Add YourTest to the test/test.cfg file.
  • You can use @YourTest to execute only YourTest. For example, if you need to execute only MountainCar test cases, use @MountainCarTest.

Test Configuration

The test cases are executed using:

  • 64-bit machines:

    • ./configure_m64
    • make
    • ./RLLibTest
  • 32-bit machines:

    • ./configure_m32
    • make
    • ./RLLibTest
  • Debugging:

    • ./configure_debug
    • make
    • ./RLLibTest

Visualization

RLLib provides a QT4.8 based Reinforcement Learning problems and algorithms visualization tool named RLLibViz. Currently RLLibViz visualizes following problems and algorithms:

  • On-policy:

    • SwingPendulum problem with continuous actions. We use AverageRewardActorCritic algorithm.
  • Off-policy:

    • ContinuousGridworld and MountainCar problems with discrete actions. We use Off-PAC algorithm.
  • In order to run the visualization tool, you need to have QT4.8 installed in your system.

  • In order to install RLLibViz:

    • Change directory to visualization/RLLibViz
    • ./configure
    • ./RLLibVizSwingPendulum
    • ./RLLibVizContinuousGridworld
    • ./RLLibVizMountainCar

Documentation

Publication

Dynamic Role Assignment using General ValueFunctions

Contact

Saminda Abeyruwan (saminda@cs.miami.edu)

Changes to previous version:

Current release version is v2.0.

BibTeX Entry: Download
Corresponding Paper BibTeX Entry: Download
URL: Project Homepage
Supported Operating Systems: Linux, Platform Independent, Windows Under Cygwin
Data Formats: Bin
Tags: Lightweight, Off Policy, On Policy, Reinforcement Learning Library, Standard
Archive: download here

Other available revisons

Version Changelog Date
2.0

Current release version is v2.0.

April 25, 2014, 02:58:32
1.6

Current release version is v1.6.

December 1, 2013, 10:21:06
1.5

Current release version is v1.5.

November 20, 2013, 01:45:44
1.0.2

Current release version is v1.0.2.

October 21, 2013, 05:58:19
1.0.1

Current release version is v1.0.1.

October 8, 2013, 22:42:34
1.0

Initial Announcement on mloss.org.

September 16, 2013, 02:31:21

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