Project details for HierLearning

Logo HierLearning 1.0

by neville - March 2, 2014, 04:24:37 CET [ BibTeX BibTeX for corresponding Paper Download ]

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Description:

HierLearning is a C++11 implementation of a general-purpose, multi-agent, hierarchical reinforcement learning system for sequential decision problems. It was created as a platform for HierGen, an algorithm for hierarchical structure discovery in sequential decision problems.


Features

  • Facilitates the implementation of hierarchical and non-hierarchical learning algorithms.
  • Incorporates multi-agent learning.
  • Facilitates the implementation of sequential decision problems.

Requirements

(The versions that HierLearning has been verified on are mentioned in parentheses.)

  • Compiler: Visual Studio (2012, v11) or gcc (v4.8.1)
  • Weka (v3.6.5)
  • Python (v2.7)

Optional: * Graphviz (v2.28) Wargus (v2.1) Octave (v3.2.4)


Installation

To build binary: make

To clean: make clean


Usage

hierlearning -h
hierlearning -d <domain> -l <learner> [-r <number of runs> -e <number of episodes>]
hierlearning -d <domain> -n <number of trajectories> -t <trajectory filename>
hierlearning -d <domain> -l <learner> -n <number of trajectories> [-m <model directory>] [-r <number of runs> -e <number of episodes>]
hierlearning -d <domain> -l <learner> -t <trajectory file> [-m <model directory>] [-r <number of runs> -e <number of episodes>]

Examples

To load the manually-designed hierarchy and execute 10 runs of 100 episodes each: hierlearning -d taxi -l maxq -r 10 -e 100

To generate 50 random trajectories: hierlearning -d taxi -n 50 -t trajectory.out

To read the trajectory file and generate the task hierarchy based on the supplied models: hierlearning -d taxi -l maxq -t trajectory.out -m models

To generate 50 random trajectories, build the task hierarchy, and execute 10 runs of 100 episodes each: hierlearning -d taxi -l maxq -n 50 -r 10 -e 100


Execution

Run on a cluster using qsub: cluster [HTML_REMOVED] [HTML_REMOVED] [HTML_REMOVED] [HTML_REMOVED] [HTML_REMOVED]

Process the output (needs Octave): process_results [HTML_REMOVED] [HTML_REMOVED] [HTML_REMOVED]

Changes to previous version:

Initial Announcement on mloss.org.

BibTeX Entry: Download
Corresponding Paper BibTeX Entry: Download
Supported Operating Systems: Linux, Windows, Unix
Data Formats: Any
Tags: Markov Decision Process, Multiagent System, Hierarchical Reinforcement Learning
Archive: download here

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