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RLLib: C++ Template Library to Learn Behaviors and Represent Learnable Knowledge using On/Off Policy Reinforcement Learning
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@samindaa samindaa released this 08 Oct 20:32
· 168 commits to master since this release
v1.0.1
d329994

RLLib

(C++ Template Library to 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 a highly optimized library that is designed and written specifically for robotic applications. 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)
    SARSA(lambda)
    Expected-SARSA(lambda)
    Actor-Critic (continuous and discrete actions, discounted, averaged reward settings, etc.)
  • Supervised learning algorithms:
    Adaline
    IDBD
    SemiLinearIDBD
    Autostep
  • Policies:
    Random
    Random50%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
    Helicopter
    Continuous Grid World
  • 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.

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"
#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.

Test Configuration

The test cases are executed using:

./configure
make
./RLLibTest

Documentation

Contact

Saminda Abeyruwan (saminda@cs.miami.edu)

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