Network Management Research Group M-S. Kim
Internet-Draft Y-G. Hong
Intended status: Informational ETRI
Expires: May 2, 2018 Y-H. Han
KoreaTech
October 29, 2017
Intelligent Management using Collaborative Reinforcement Multi-agent
System
draft-kim-nmrg-rl-01
Abstract
This document describes an intelligent reinforcement learning agent
system to autonomously manage agent path-planning over a
communication network. The main centralized node called by the
global environment should not only manage all agents workflow in a
hybrid peer-to-peer networking architecture and, but transfer and
share information in distributed nodes. All agents in distributed
nodes are able to be provided with a cumulative reward for each
action that a given agent takes with respect to an optimized
knowledge based on a to-be-learned policy over the learning process.
The optimized knowledge would be involved with a large state
information by the control action. A reward from the global
environment is reflected to the next optimized control action for
autonomous path management in distributed networking nodes. The
reinforcement learning process (RLP) have developed and expanded to
deep reinforcement learning (DRL) with a data-driven approach
technique for learning process. The trendy technique has been widely
to attempt and apply to networking fields since DRL can be used in
practice, since networking areas have the dynamics and heterogeneous
environment disturbances, so that in the technique is able to be
intelligently learned in the effective strategy.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3
2. Conventions and Terminology . . . . . . . . . . . . . . . . . 4
3. Motivation . . . . . . . . . . . . . . . . . . . . . . . . . 4
3.1. General Motivation for Reinforcement Learning (RL) . . . 4
3.2. Reinforcement Learning (RL) in networks . . . . . . . . . 4
3.3. Deep Reinforcement Learning (DRL) in networks . . . . . . 4
3.4. Motivation in our work . . . . . . . . . . . . . . . . . 4
4. Related Works . . . . . . . . . . . . . . . . . . . . . . . . 5
4.1. Autonomous Driving System . . . . . . . . . . . . . . . . 5
4.2. Game Theory . . . . . . . . . . . . . . . . . . . . . . . 5
4.3. Wireless Sensor Network (WSN) . . . . . . . . . . . . . . 5
4.4. Routing Enhancement . . . . . . . . . . . . . . . . . . . 5
4.5. Routing Optimization . . . . . . . . . . . . . . . . . . 6
5. Multi-agent Reinforcement Learning (RL) Technologies . . . . 6
5.1. Reinforcement Learning (RL) . . . . . . . . . . . . . . . 6
5.2. Policy using Distance and Frequency . . . . . . . . . . . 6
5.3. Distributed Computing Node . . . . . . . . . . . . . . . 6
5.4. Agent Sharing Information . . . . . . . . . . . . . . . . 7
5.5. Deep Learning Technique . . . . . . . . . . . . . . . . . 7
5.6. Sub-goal Selection . . . . . . . . . . . . . . . . . . . 8
6. Proposed Architecture for Deep Reinforcement Learning (DRL) . 8
7. Use case of Multi-agent Reinforcement Learning (RL) . . . . . 9
7.1. Distributed Multi-agent Reinforcement Learning: Sharing
Information Technique . . . . . . . . . . . . . . . . . . 9
7.2. Use case of Shortest Path-planning via sub-goal selection 11
8. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 11
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9. Security Considerations . . . . . . . . . . . . . . . . . . . 11
10. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . 12
11. References . . . . . . . . . . . . . . . . . . . . . . . . . 12
11.1. Normative References . . . . . . . . . . . . . . . . . . 12
11.2. Informative References . . . . . . . . . . . . . . . . . 12
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 14
1. Introduction
In large infrastructures such as transportation, health and energy
systems, collaborative monitoring system is needed, where there are
special needs for intelligent distributed networking systems with
learning schemes. Agent Reinforcement Learning (RL) for
intelligently autonomous network management, in general, is one of
the challengeable methods in a dynamic complex cluttered environment
over a network. It also needs the development of computational
multi-agents learning systems in large distributed networking nodes,
where the agents have limited and incomplete knowledge, and they only
access local information in distributed networking nodes.
Reinforcement Learning (RL) can become an effective technique to
transfer and share information among agents, as it does not require a
priori knowledge of the agent behavior or environment to accomplish
its tasks [Megherbi]. Such a knowledge is usually acquired and
learned automatically and autonomously by trial and error.
Reinforcement Learning (RL) is Machine Learning techniques that will
be adapted to the various networking environments for automatic
networks[S. Jiang]. Thus, this document provides motivation,
learning technique, and use case for network machine learning.
Deep reinforcement learning (DRL) recently proposes that the extended
reinforcement learning (RL) algorithm could emerge as a powerful
data-driven technique over a large state space to overcome the
classical behavior RL process. The DRL technique has been
significantly shown as successful models in playing Atari games [V.
Mnih]. The DRL provides more effective experimental system
performance in a complex and cluttered networking environment.
Classical reinforcement learning (RL) slightly has a limitation to be
adopted in networking areas, since the networking environments
consist of significantly large and complex components in fields of
routing configuration, optimization and system management, so that
DRL provided with much more state information for learning process is
needed.
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2. Conventions and Terminology
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
document are to be interpreted as described in [RFC2119].
3. Motivation
3.1. General Motivation for Reinforcement Learning (RL)
Reinforcement Learning (RL) is a system capable of autonomous
acquirement and incorporation of knowledge. It can continuously
self-improve learning process with experience and attempts to
maximize cumulative reward to manage an optimized learning knowledge
by multi-agents-based monitoring systems[Teiralbar]. The maximized
reward can be increasingly optimizing of learning speed for agent
autonomous learning process.
3.2. Reinforcement Learning (RL) in networks
Reinforcement learning (RL) is an emerging technology in terms of
monitoring network system to achieve fair resource allocation for
nodes within the wire or wireless mesh setting. Monitoring
parameters of the network and adjusts based on the network dynamics
can demonstrate to improve fairness in wireless environment
Infrastructures and Resources[Nasim].
3.3. Deep Reinforcement Learning (DRL) in networks
Deep reinforcement learning is a large state data-driven approach on
an intelligently learning strategy. The intelligent technique
represents learning models successfully to learn control policies
directly from high-dimensional sensory input using reinforcement
learning (RL) with Q-value function in a convolutional neural network
[Mnih]. The model repeatedly estimates future reward to acquire more
effective control action in following next steps. The DRL can be
widely-adopted in routing optimization to attempt minimizing the
network delay [Stampa].
3.4. Motivation in our work
There are many different networking management issues such as
connectivity, traffic management, fast internet without latency and
etc. We expect that ml-based mechanism such as reinforcement
learning [RL] will provide network solutions with multiple cases
against human operating capacities even if it is a challengeable area
due to a multitude of reasons such as large state space search,
complexity in giving reward, difficulty in agent action selection,
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and difficulty in sharing and merging learned information among the
agents in a distributed memory node to be transferred over a
communication network.[Minsuk]
4. Related Works
4.1. Autonomous Driving System
Autonomous vehicle is capable of self-automotive driving without
human supervision depending on optimized trust region policy by
reinforcement learning (RL) that enables learning of more complex and
special network management environment. Such a vehicle provides a
comfortable user experience safely and reliably on interactive
communication network [April], [Markus].
4.2. Game Theory
The adaptive multi-agent system, which is combined with complexities
from interacting game player, has developed in a field of
reinforcement learning (RL). In the early game theory, the
interdisciplinary work was only focused on competitive games, but
Reinforcement Learning (RL) has developed into a general framework
for analyzing strategic interaction and has been attracted field as
diverse as psychology, economics and biology.[Ann] AlphaGo is also
one of the game theories using reinforcement learning (RL), developed
by Google DeepMind. Even though it began as a small learning
computational program with some simple actions, it has now trained on
a policy and value networks of thirty million actions, states and
rewards.
4.3. Wireless Sensor Network (WSN)
Wireless sensor network (WSN) consists of a large number of sensors
and sink nodes for monitoring systems with event parameters such as
temperature, humidity, air conditioning, etc. Reinforcement learning
(RL) in WSNs has been applied in a wide range of schemes such as
cooperative communication, routing and rate control. The sensors and
sink nodes are able to observe and carry out optimal actions on their
respective operating environment for network and application
performance enhancements.[Kok-Lim]
4.4. Routing Enhancement
Reinforcement Learning (RL) is used to enhance multicast routing
protocol in wireless ad hoc networks, where each node has different
capability. Routers in the multicast routing protocol are determined
to discover optimal route with a predicted reward, and then the
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routers create the optimal path with multicast transmissions to
reduce the overhead in Reinforcement Learning (RL).[Kok-Lim]
4.5. Routing Optimization
Routing optimization as traffic engineering is one of the important
issues to control the behavior of transmitted data in order to
maximize the performance of network [Stampa]. There are several
attempts to be adopted with machine learning algorithms in the
context of routing optimization. Deep reinforcement learning (DRL)
is recently one of solutions for unseen network states that cannot be
achieved by traditional table-based RL agent [Stampa]. DRL can
provide more improvement to optimal control routing configuration by
given-agent on complex networking.
5. Multi-agent Reinforcement Learning (RL) Technologies
5.1. Reinforcement Learning (RL)
Agent reinforcement Learning (RL) is ml-based unsupervised algorithms
based on an agent learning process. Reinforcement Learning (RL) is
normally used with a reward from centralized node (the global
environment), and capable of autonomous acquirement and incorporation
of knowledge. It is continuously self-improving and becoming more
efficient as the learning process from an agent experience to
optimize management performance for autonomous learning
process.[Sutton][Madera]
5.2. Policy using Distance and Frequency
Distance and Frequency algorithm uses the state occurrence frequency
in addition to the distance to goal. It avoids deadlocks and lets
the agent escape the Dead, and it was derived to enhance agent
optimal learning speed. Distance-and-Frequency is based on more
levels of agent visibility to enhance learning algorithm by an
additional way that uses the state occurrence frequency.[Al-Dayaa]
5.3. Distributed Computing Node
Autonomous multi-agent learning process for network management
environment is related to transfer optimized knowledge between agents
on a given local node or distributed memory nodes over a
communication network.
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5.4. Agent Sharing Information
This is a technique how agents can share information for optimal
learning process. The quality of agent decision making often depends
on the willingness of agents to share a given learning information
collected by agent learning process. Sharing Information means that
an agent would share and communicate the knowledge learned and
acquired with or to other agents using reinforcement learning.
Agents normally have limited resources and incomplete knowledge
during learning exploration. For that reason, the agents should take
actions and transfer the states to the global environment under
reinforcement learning (RL), then it would share the information with
other agents, where all agents explore to reach their goals via a
distributed reinforcement reward-based learning method on the
existing local distributed memory nodes.
MPI (Message Passing Interface) is used for communication way. Even
if the agents do not share the capabilities and resources to monitor
an entire given large terrain environment, they are able to share the
needed information to manage collaborative learning process for
optimized management in distributed networking
nodes.[Chowdappa][Minsuk]
5.5. Deep Learning Technique
Recently, some of advanced techniques using RL encounter and combine
to deep learning in neural network that has made it possible to
extract high-level features from raw data in compute vision
[Krizhevsky]. There are many challenges under the deep learning
models such as Convolution Neural Network, Recurrent Neural Network
and etc. The benefit of the deep learning applications is that lots
of networking models, which have problematic issue due to complex and
cluttered networking structure, can be used with large amounts of
labelled training data.
DRL can provide more extended and powerful scenarios to build
networking models with optimized action controls, huge system states
and real-time-based reward function. Moreover, DRL has a significant
advantage to set highly sequential data in a large model state space.
In particular, the data distribution in RL is able to change as
learning behaviors, that is a problem for deep learning approaches
assumed by a fixed underlying distribution [Mnih].
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5.6. Sub-goal Selection
A new technical method for agent sub-goal selection in distributed
nodes is introduced to reduce the agent initial random exploration
with a given selected sub-goal.
[TBD]
6. Proposed Architecture for Deep Reinforcement Learning (DRL)
The architecture using Reinforcement Learning (RL) describes a
collaborative multi-agent-based system in distributed environments as
shown in figure 1, where the architecture is combined with a hybrid
architecture making use of both a master and slave architecture and a
peer-to-peer. The centralized node(global environment), assigns each
slave computing node a portion of the distributed terrain and an
initial number of agents.
+-------------+
| | +-----------------+
| |<...... node 1 .......>| terrain 1 |
| | +-----------------+
| Global env. | + |
| (node 0) | | |
| | | +
| | +-----------------+
| |<...... node 2 .......>| terrain 2 |
| | +-----------------+
+-------------+
Figure 1: Hybrid P2P and Master/Slave Architecture Overview
Reinforcement Learning (RL) actions involve interacting with a given
environment, so the environment provides an agent learning process
with the elements as followings:
o Agent control actions, large states and cumulative rewards
o Initial data-set in memory
o Random or learning process in a given node
o Next, optimamization in neural network under reinforcement
learning (RL)
Additionally, agent actions with states toward its goal as below:
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o Agent continuously control actions to earn next optimized state
based on its policy with reward
o After an agent reaches its goal, it can repeatedly collect the
information collected by the random or learning process to next
learning process for optimal management
o Agent learning process is optimized in the following phase and
exploratory learning trials
As shown in Figure2, we illustrate the fundamental architecture for
relationship of a control action, large states space and optimized
reward. The agent does an action that leads to a reward from
achieving an optimal path toward its goal. Our works will be
extended depending on the architecture.
DRL Network
+----------------------------------+
|Q-Value1| |
|--------+ +-------+ +------+|
...........Action...........|Q-Value2|----|Network|----|States||<......
. |--------+ +-------+ +------+| .
. |Q-Value3| | .
. +----------------------------------+ .
. .
+---------+----------+ .
| Global Environment | .
+---------+----------+ .
. .
. .
. +-------------------+ +----------+
................>+ Large State Space +............States.......>+ D-Memory +
+-------------------+ +----------+
Figure 2: DRL work-flow Overview
7. Use case of Multi-agent Reinforcement Learning (RL)
7.1. Distributed Multi-agent Reinforcement Learning: Sharing
Information Technique
In this section, we deal with case of a collaborative distributed
multi-agent, where each agent has same or different individual goals
in a distributed environment. Since sharing information scheme among
the agents is problematic one, we need to expand on the work
described by solving the challenging cases.
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Basically, the main proposed algorithm is presented by distributed
multi-agent reinforcement learning as below:
+-------------------------------------------------------------------+
| Proposed Algorithm |
+-------------------------------------------------------------------+
| (1) Let Ni denote the number of node (i= 1, 2, 3 ...) |
| |
| (2) Let Aj denote the number of agent |
| |
| (3) Let Dk denote the number of goals |
| |
| (4) Place initial number of agents Aj, in random position (Xm, |
| Yn) |
| |
| (5) Initialization of data-set memory for neural network |
| |
| (6) Copy neutal network Q and store as the data-set memory |
| |
| (7) Every Aj in Ni |
| |
| -----> (a) Do initial exploration (random) to corresponding Dk |
| |
| -----> (b) Do exploration (using RL) for Tx denote the number of |
| trial |
+-------------------------------------------------------------------+
Table 1: Proposed Algorithm
+-------------------------------------------------------------------+
| Random Trial |
+-------------------------------------------------------------------+
| (1) Let Si denote the the current state |
| |
| (2) Relinquish Si so that the other agent can occupy the position |
| |
| (3) Assign the agent new position |
| |
| (4) Update the current state Si -> Si+1 |
+-------------------------------------------------------------------+
Table 2: Random Trial
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+-------------------------------------------------------------------+
| Optimal Trial |
+-------------------------------------------------------------------+
| (1) Let Si denote the the current state |
| |
| (2) Let ACj denote a contorl action |
| |
| (3) Let DRm denote discount reward |
| |
| (4) Choose ACj <- Policy(Si, ACj) in neural network |
| |
| (5) Update and copy the network for learning process in the |
| global environment |
| |
| (6) Update the current state Si < Si+1- |
| |
| (7) Repeat a available network control action |
+-------------------------------------------------------------------+
Table 3: Optimal Trial
Multi-agent reinforcement learning (RL) in distributed nodes can
improve the overall system performance to transfer or share
information from one node to another node in following cases;
expanded complexity in RL technique with various experimental factors
and conditions, analyzing multi-agent sharing information for agent
learning process.
7.2. Use case of Shortest Path-planning via sub-goal selection
Sub-goal selection is a scheme of a distributed multi-agent RL
technique based on selected intermediary agent sub-goal(s) with the
aim of reducing the initial random trial. The scheme is to improve
the multi-agent system performance with asynchronously triggered
exploratory phase(s) with selected agent sub-goal(s) for autonomous
network management.
[TBD]
8. IANA Considerations
There are no IANA considerations related to this document.
9. Security Considerations
[TBD]
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10. Acknowledgements
Carles Gomez has been funded in part by the Spanish Government
(Ministerio de Educacion, Cultura y Deporte) through the Jose
Castillejo grant CAS15/00336. His contribution to this work has been
carried out during his stay as a visiting scholar at the Computer
Laboratory of the University of Cambridge.
11. References
11.1. Normative References
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119,
DOI 10.17487/RFC2119, March 1997,
<https://www.rfc-editor.org/info/rfc2119>.
11.2. Informative References
[I-D.jiang-nmlrg-network-machine-learning]
Jiang, S., "Network Machine Learning", ID draft-jiang-
nmlrg-network-machine-learning-02, October 2016.
[Megherbi]
"Megherbi, D. B., Kim, Minsuk, Madera, Manual., A Study of
Collaborative Distributed Multi-Goal and Multi-agent based
Systems for Large Critical Key Infrastructures and
Resources (CKIR) Dynamic Monitoring and Surveillance, IEEE
International Conference on Technologies for Homeland
Security", 2013.
[Teiralbar]
"Megherbi, D. B., Teiralbar, A. Boulenouar, J., A Time-
varying Environment Machine Learning Technique for
Autonomous Agent Shortest Path Planning, Proceedings of
SPIE International Conference on Signal and Image
Processing, Orlando, Florida", 2001.
[Nasim] "Nasim ArianpooEmail, Victor C.M. Leung, How network
monitoring and reinforcement learning can improve tcp
fairness in wireless multi-hop networks, EURASIP Journal
on Wireless Communications and Networking", 2016.
[Minsuk] "Dalila B. Megherbi and Minsuk Kim, A Hybrid P2P and
Master-Slave Cooperative Distributed Multi-Agent
Reinforcement Learning System with Asynchronously
Triggered Exploratory Trials and Clutter-index-based
Selected Sub goals, IEEE CIG Conference", 2016.
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[April] "April Yu, Raphael Palefsky-Smith, Rishi Bedi, Deep
Reinforcement Learning for Simulated Autonomous Vehicle
Control, Stanford University", 2016.
[Markus] "Markus Kuderer, Shilpa Gulati, Wolfram Burgard, Learning
Driving Styles for Autonomous Vehicles from Demonstration,
Robotics and Automation (ICRA)", 2015.
[Ann] "Ann Nowe, Peter Vrancx, Yann De Hauwere, Game Theory and
Multi-agent Reinforcement Learning, In book: Reinforcement
Learning: State of the Art, Edition: Adaptation, Learning,
and Optimization Volume 12", 2012.
[Kok-Lim] "Kok-Lim Alvin Yau, Hock Guan Goh, David Chieng, Kae
Hsiang Kwong, Application of reinforcement learning to
wireless sensor networks: models and algorithms, Published
in Journal Computing archive Volume 97 Issue 11, Pages
1045-1075", November 2015.
[Sutton] "Sutton, R. S., Barto, A. G., Reinforcement Learning: an
Introduction, MIT Press", 1998.
[Madera] "Madera, M., Megherbi, D. B., An Interconnected Dynamical
System Composed of Dynamics-based Reinforcement Learning
Agents in a Distributed Environment: A Case Study,
Proceedings IEEE International Conference on Computational
Intelligence for Measurement Systems and Applications,
Italy", 2012.
[Al-Dayaa]
"Al-Dayaa, H. S., Megherbi, D. B., Towards A Multiple-
Lookahead-Levels Reinforcement-Learning Technique and Its
Implementation in Integrated Circuits, Journal of
Artificial Intelligence, Journal of Supercomputing. Vol.
62, issue 1, pp. 588-61", 2012.
[Chowdappa]
"Chowdappa, Aswini., Skjellum, Anthony., Doss, Nathan,
Thread-Safe Message Passing with P4 and MPI, Technical
Report TR-CS-941025, Computer Science Department and NSF
Engineering Research Center, Mississippi State
University", 1994.
[Mnih] "V.Mnih and et al., Human-level Control Through Deep
Reinforcement Learning, Nature 518.7540", 2015.
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[Stampa] "G Stamp, M Arias, etc., A Deep-reinforcement Learning
Approach for Software-defined Networking Routing
Optimization, cs.NI", 2017.
[Krizhevsky]
"A Krizhevsky, I Sutskever, and G Hinton, Imagenet
classification with deep con- volutional neural networks,
In Advances in Neural Information Processing Systems,
1106-1114", 2012.
Authors' Addresses
Min-Suk Kim
Etri
161 Gajeong-Dong Yuseung-Gu
Daejeon 305-700
Korea
Phone: +82 42 860 5930
Email: mskim16@etri.re.kr
Yong-Geun Hong
ETRI
161 Gajeong-Dong Yuseung-Gu
Daejeon 305-700
Korea
Phone: +82 42 860 6557
Email: yghong@etri.re.kr
Youn-Hee Han
KoreaTech
Byeongcheon-myeon Gajeon-ri, Dongnam-gu
Choenan-si, Chungcheongnam-do
330-708
Korea
Phone: +82 41 560 1486
Email: yhhan@koreatech.ac.kr
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