Reinforcement LearningTheory and Applications pptx

Summary of Theory: Chapters 1 and 2 create a link to supervised and unsupervised learning, respectively, by garding reinforcement learning as a prediction problem, and chapter 3 looks at

Reinforcement Learning

Theory and Applications

Reinforcement Learning

Theory and Applications

Edited by Cornelius Weber Mark Elshaw Norbert Michael Mayer

I-TECH Education and Publishing

Published by the I-Tech Education and Publishing, Vienna, Austria

Abstracting and non-profit use of the material is permitted with credit to the source Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher No responsibility is accepted for the accuracy of information contained in the published articles Publisher assumes no responsibility liability for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained inside After this work has been published by the Advanced Robotic Systems International, authors have the right to republish it, in whole or part, in any publication of which they are an author or editor, and the make other personal use of the work

© 2008 I-Tech Education and Publishing

A catalog record for this book is available from the Austrian Library

Reinforcement Learning, Theory and Applications, Edited by Cornelius Weber, Mark Elshaw and bert Michael Mayer

p cm

ISBN 978-3-902613-14-1

1 Reinforcement Learning 2 Theory 3 Applications

Preface

Brains rule the world, and brain-like computation is increasingly used in computers and lectronic devices Brain-like computation is about processing and interpreting data or direct-

e-ly putting forward and performing actions Learning is a very important aspect This book is

on reinforcement learning which involves performing actions to achieve a goal Two other learning paradigms exist Supervised learning has initially been successful in prediction and classification tasks, but is not brain-like Unsupervised learning is about understanding the world by passively mapping or clustering given data according to some order principles, and is associated with the cortex in the brain In reinforcement learning an agent learns by trial and error to perform an action to receive a reward, thereby yielding a powerful method

to develop goal-directed action strategies It is predominately associated with the basal ganglia in the brain

The first 11 chapters of this book, Theory, describe and extend the scope of reinforcement learning The remaining 11 chapters, Applications, show that there is already wide usage in numerous fields Reinforcement learning can tackle control tasks that are too complex for traditional, hand-designed, non-learning controllers As learning computers can deal with technical complexities, the tasks of human operators remain to specify goals on increasingly higher levels

This book shows that reinforcement learning is a very dynamic area in terms of theory and applications and it shall stimulate and encourage new research in this field We would like

to thank all contributors to this book for their research and effort

Summary of Theory:

Chapters 1 and 2 create a link to supervised and unsupervised learning, respectively, by garding reinforcement learning as a prediction problem, and chapter 3 looks at fuzzy-control with a reinforcement-based genetic algorithm Reinforcement algorithms are modi-fied in chapter 4 for future parallel and quantum computing, and in chapter 5 for a more ge-neral class of state-action spaces, described by grammars Then follow biological views; in chapter 6 how reinforcement learning occurs on a single neuron level by considering the in-teraction between a spatio-temporal learning rule and Hebbian learning, and in a global brain view of chapter 7, unsupervised learning is depicted as a means of data pre-processing and arrangement for reinforcement algorithms A table presents a ready-to-implement desc-ription of standard reinforcement learning algorithms The following chapters consider mul-

re-ti agent systems where a single agent has only parre-tial view of the enre-tire system Mulre-tiple agents can work cooperatively on a common goal, as considered in chapter 8, or rewards can be individual but interdependent, such as in game play, as considered in chapters 9, 10 and 11

21 presents a physicians' decision support system for diagnosis and treatment, involving a knowledgebase server In chapter 22 a reinforcement learning sub-module improves the ef-ficiency for the exchange of messages in a decision support system in air traffic manage-ment

Mark Elshaw Norbert Michael Mayer

Contents

Preface V

1 Neural Forecasting Systems 001

Takashi Kuremoto, Masanao Obayashi and Kunikazu Kobayashi

2 Reinforcement learning in system identification 021

Mariela Cerrada and Jose Aguilar

3 Reinforcement Evolutionary Learning for Neuro-Fuzzy Controller Design 033

Cheng-Jian Lin

4 Superposition-Inspired

Reinforcement Learning and Quantum Reinforcement Learning 059

Chun-Lin Chen and Dao-Yi Dong

5 An Extension of Finite-state Markov

Decision Process and an Application of Grammatical Inference 085

Takeshi Shibata and Ryo Yoshinaka

6 Interaction between the Spatio-Temporal Learning Rule (non Hebbian)

and Hebbian in Single Cells: A cellular mechanism of reinforcement learning 105

Minoru Tsukada

7 Reinforcement Learning Embedded in Brains and Robots 119

Cornelius Weber, Mark Elshaw, Stefan Wermter, Jochen Triesch and Christopher Willmot

8 Decentralized Reinforcement Learning

for the Online Optimization of Distributed Systems 143

Jim Dowling and Seif Haridi

9 Multi-Automata Learning 167

Verbeeck Katja, Nowe Ann, Vrancx Peter and Peeters Maarten

10 Abstraction for Genetics-based Reinforcement Learning 187

Will Browne, Dan Scott and Charalambos Ioannides

11 Dynamics of the Bush-Mosteller learning algorithm in 2x2 games 199

Luis R Izquierdo and Segismundo S Izquierdo

12 Modular Learning Systems for

Behavior Acquisition in Multi-Agent Environment 225

Yasutake Takahashi and Minoru Asada

13 Optimising Spoken Dialogue

Strategies within the Reinforcement Learning Paradigm 239

Olivier Pietquin

14 Water Allocation Improvement in

River Basin Using Adaptive Neural Fuzzy Reinforcement Learning Approach 257

Abolpour B., Javan M and Karamouz M

15 Reinforcement Learning for Building Environmental Control 283

Konstantinos Dalamagkidis and Dionysia Kolokotsa

16 Model-Free Learning Control of Chemical Processes 295

S Syafiie, F Tadeo and E Martinez

17 Reinforcement Learning-Based

Supervisory Control Strategy for a Rotary Kiln Process 311

Xiaojie Zhou, Heng Yue and Tianyou Chai

18 Inductive Approaches based on

Trial/Error Paradigm for Communications Network 325

Abdelhamid Mellouk

19 The Allocation of Time and Location Information to

Activity-Travel Sequence Data by means of Reinforcement Learning 359

Wets Janssens

20 Application on Reinforcement

Learning for Diagnosis based on Medical Image 379

Stelmo Magalhaes Barros Netto, Vanessa Rodrigues Coelho Leite,

Aristofanes Correa Silva, Anselmo Cardoso de Paiva and Areolino de Almeida Neto

21 RL based Decision Support System for u-Healthcare Environment 399

Devinder Thapa, In-Sung Jung, and Gi-Nam Wang

22 Reinforcement Learning to

Support Meta-Level Control in Air Traffic Management 409

Daniela P Alves, Li Weigang and Bueno B Souza

Neural Forecasting Systems

Takashi Kuremoto, Masanao Obayashi and Kunikazu Kobayashi

or nonlinear models which need to be constructed by advanced mathematic techniques and long process to find optimized parameters of models The good ability of function approximation and strong performance of sample learning of NN have been known by using error back propagation learning algorithm (BP) with a feed forward multi-layer NN called multi-layer perceptron (MLP) (Rumelhart et al, 1986), and after this mile stone of neural computing, there have been more than 5,000 publications on NN for forecasting (Crone & Nikolopoulos, 2007)

To simulate complex phenomenon, chaos models have been researched since the middle of last century (Lorenz, 1963; May, 1976) For NN models, the radial basis function network (RBFN) was employed on chaotic time series prediction in the early time (Casdagli, 1989)

To design the structure of hidden-layer of RBFN, a cross-validated subspace method is proposed, and the system was applied to predict noisy chaotic time series (Leung & Wang, 2001) A two-layered feed-forward NN, which has its all hidden units with hyperbolic tangent activation function and the final output unit with linear function, gave a high accuracy of prediction for the Lorenz system, Henon and Logistic map (Oliveira et al, 2000)

To real data of time series, NN and advanced NN models (Zhang, 2003) are reported to provide more accurate forecasting results comparing with traditional statistical model (i.e the autoregressive integrated moving average (ARIMA)(Box & Jankins, 1976)), and the performances of different NNs for financial time series are confirmed by Kodogiannis & Lolis (Kodogiannis & Lolis, 2002) Furthermore, using benchmark data, several time series forecasting competitions have been held in the past decades, many kinds of NN methods showed their powerful ability of prediction versus other new techniques, e.g vector quantization, fuzzy logic, Bayesian methods, Kalman filter or other filtering techniques, support vector machine, etc (Lendasse et al, 2007; Crone & Nikolopoulos, 2007)

Meanwhile, reinforcement learning (RL), a kind of goal-directed learning, has been generally applied in control theory, autonomous system, and other fields of intelligent computation (Sutton & Barto, 1998) When the environment of an agent belongs to Markov decision process (MDP) or the Partially Observable Markov Decision Processes (POMDP), behaviours of exploring let the agent obtain reward or punishment from the environment, and the policy of action then is modified to adapt to acquire more reward When prediction

error for a time series is considered as reward or punishment from the environment, one can

use RL to train predictors constructed by neural networks

In this chapter, two kinds of neural forecasting systems using RL are introduced in detail: a

self-organizing fuzzy neural network (SOFNN) (Kuremoto et al., 2003) and a multi-layer

perceptron (MLP) predictor (Kuremoto et al., 2005) The results of experiments using Lorenz

chaos showed the efficiency of the method comparing with the results by a conventional

learning method (BP)

2 Architecture of neural forecasting system

The flow chart of neural forecasting processing is generally used by which in Fig 1 The tth

step time series data y ( t) can be embedded into a new n-dimensional space x( )t according

to Takens Theorem (Takens, 1981) Eq (1) shows the detail of reconstructed vector space

which serves input layer of NN, here τ is an arbitrary delay An example of 3-dimensional

reconstruction is shown in Fig 2 The output layer of neural forecasting systems is usually

with one neuron whose output yˆ(t +1) equals prediction result

) ) 1 ( ( , ), ( ), ( (

)) ( , ), ( ), ( 1 ( ) (

y t y

t x t x t x t

There are various architectures of NN models, including MLP, RBFN, recurrent neural network (RNN), autoregressive recurrent neural network (ARNN), neuro-fuzzy hybrid network, ARIMA-NN hybrid model, SOFNN, and so on The training rules of NNs are also very different not only well-known methods, i.e., BP, orthogonal least squares (OLS), fuzzy inference, but also evolutional computation, i.e., genetic algorithm (GA), particle swarm optimization (PSO), genetic programming (GP), RL, and so on

Fig 2 Embedding a time series into a 3-dimensional space

2.1 MLP with BP

MLP, a feed-forward multi-layer network, is one of the most famous classical neural forecasting systems whose structure is shown in Fig 3 BP is commonly used as its learning rule, and the system performs fine efficiency in the function approximation and nonlinear prediction

For the hidden layer, let the number of neurons is K, the output of neuron k is H k, then the output of MLP is obtained by Eq (2) and Eq (3)

) (

) 1 (

1

k yk K

k

H w f

1

t x w f

input neurons, respectively Activation function f (u) is a sigmoid function (or hyperblolic

tangent function) given by Eq (4)

) exp(

1

1 )

(

u u

f

β

− +

Gradient parameter β is usually set to 1.0, and to correspond to f (u), the scale of time series

data should be adjusted to (0.0, 1.0)

BP is a supervised learning algorithm, using sample data trains NN providing more correct

output data by modifying all of connections between layers Conventionally, the error

function is given by the mean square error as Eq (5)

∑−

=

+

− +

0

2

)) 1 ( ) 1 ( ( 1 ) (

S

t

t y t

y S W

Here S is the size of train data set, y (t+1) is the actual data in time series The error is

minimized by adjusting the weights according to Eq (6), Eq (7) and Eq (2), Eq (3)

) , ( )

, ( )

,

ik yk new

ik

w

) / , / ( ) , ( wyk wik E wyk E wik

w w

Fig 4 A MLP with n input neurons, two hidden layers, and one neuron in output layer

using RL training algorithm

2.2 MLP with RL

One important feature of RL is its statistical action policy, which brings out exploration of adaptive solutions Fig 4 shows a MLP which output layer is designed by a neuron with Gaussian function A hidden layer consists of variables of the distribution function is added The activation function of units in each hidden layer is still sigmoid function (or hyperbolic tangent function) (Eq (8)-(10))

) exp(

1

1

β σ

∑

− +

) ) ( exp(

∑

− +

=

And the prediction value is given according to Eq (11)

1 ,

, 1 ˆ

σ

μ σ

π

Here β1,β2,β3 are gradient constants, w(wμk,wσk,w ki ) represents the connection of kth

hidden neuron with neuron μ,σ in statistical hidden layer and input neurons, respectively

The modification of w is calculated by RL algirthm which will be described in section 3

2.3 SOFNN with RL

A neuro-fuzzy hybrid forecasting system, SOFNN, using RL training algorithm is shown in

Fig 5 A hidden layer consists of fuzzy membership functions B ij(x i ( t)) is designed to

categorize input data of each dimension in x(x1(t),x2(t), , x n(t)), t = 1, 2, , S (Eq (12))

The fuzzy reference λk, which calculates the fitness for an input set x( )t , is executed by

fuzzy rules layer (Eq 13)

Fig 5 A SOFNN with n input neurons, three hidden layers, and one neuron in output layer

using RL training algorithm

) (

ij

ij i

i ij

m t x t

x B

k X t B x t

1

)) ( ( ))

( (

Where i = 1, 2, , n, j means the number of membership function which is 1 initially, m ij,σij

are the mean and standard deviation of jth membership function for input xi( t ), c means

each of membership function which connects with kth rule, respectively c ∈ j, ( j = 1, 2, , l

), and l is the maximum number of membership functions If an adaptive threshold of

(x ( t))

and rules can be realized automatically, the network owns self-organizing function to deal

with different features of inputs

The output of neurons μ,σ in stochastic layer is given by Eq (14), Eq (15) respectively

∑

∑

=

k k k

and standard deviation of stochastic function π (yˆ(t+1),w , x( )t ) whose description is

given by Eq (11) The output of system can be obtained by generating a random data

according this probability function

3 SGA of RL

3.1 Algorithm of SGA

A RL algorithm, Stochastic Gradient Ascent (SGA), is proposed by Kimura and Kobayashi

(Kimura & Kobayashi, 1996, 1998) to deal with POMDP and continuous action space

Experimental results reported that SGA learning algorithm was successful for cart-pole

control and maze problem In the case of time series forecasting, the output of predictor can

be considered as an action of agent, and the prediction error can be used as reward or

punishment from the environment, so SGA can be used to train a neural forecasting system

by renewing internal variable vector of NN (Kuremoto et al, 2003, 2005)

The SGA algorithm is given below

Step 1 Observe an input x( )t from training data of time series

Step 2 Predict a future data y ˆ ( ) t + 1 according to a probability π (yˆ( )t+1,w, x( )t)

Step 3 Receive the immediate reward rt by calculating the prediction error

( ) ( ) ( ) ( )

−

≤ +

− +

=

ε

ε 1 1

ˆ

1 1

ˆ

t y t

y if r

t y t

y if r

Here r , ε are evaluation constants greater than or equal to zero

Step 4 Calculate characteristic eligibility ei( ) t and eligibility trace D i( )t

( ) { ( y ( ) t ( ) t ) }

w t e

Here γ ( 0 ≤ γ < 1 ) is a discount factor, wi denotes ith internal variable vector

Step 5 Calculate Δ wi( ) t by Eq (19)

( ) t r b D ( ) t

Here b denotes the reinforcement baseline

Step 6 Improve policy by renewing its internal variable wby Eq (20)

( ) t

w

Here Δw ( ) t = ( Δ w1( ) t , Δ w2( ) t , L , Δ wi( ) t , L ) denotes synaptic weights, and other

internal variables of forecasting system, αs is a positive learning rate

Step 7 For next time step t+1, return to step 1

Characteristic eligibility e i( )t , shown in Eq (17), means that the change of the policy

function is concerning with the change of system internal variable vector (Williams, 1992) In

fact, the algorithm combines reward/punishment to modify the stochastic policy with its

internal variable renewing by step 4 and step 5 The finish condition of training iteration is

also decided by the enough convergence of prediction error of sample data

μ μ β

μ

μ π

π

μ μ

μ

− +

w w

e

k

k k

w k

( ) { ( ) }

) 1 ) ) 1 ( ( (

1 ) 1 (

ln ln

2 2

σ β

σ σ

π π

σ σ

σ

t y R

w w

e

k

k k

w k

(22)

( ) { ( ) } { ( ) }

) )(

1 )(

(

ln ln

ln

ki

w k w

k k i

ki k k ki

k k ki

w

e w e w R t

x

w

R R w

R R w

The initial values of wμk,wσk,w kiare random numbers in (0, 1) at the first iteration of

training Gradient constants β1,β2,β3 and reward parameters r, ε denoted by Eq (16) have

empirical values

3.3 SGA for SOFNN

For the SOFNN forecasting system described in section 2.3 (Fig 5), the characteristic

eligibility e i( )t of Eq (24)-(27) can be derived from Eq (11)-(15) with the internal viable

k k

w

t y

w w

ek

λ

λ

μ μ

π π

μ μ

μ

2

) 1 (

ln ln

k k

w

t y

w w

e

k

λ

λ σ

μ σ

σ σ

π π

σ σ

σ

) 1 ) ) 1 ( ( (

1

ln ln

2

( ) ( )

ik ij

ij i

k

k k k

k k k

ij ij

k k k

ij m

B m x B w

t y

w t

y

m

B B

m

e

ij

2 2

2 2

) 1 ) ) 1 ( ( ( 1

) 1 (

ln ln

+

−

− +

ik ij

ij i

k

k k k

k k k

ij ij

k k k

ij

B m x B w

t y

w t

y

B B

e

ij

3 2

2 2

2

) (

) 1 ) ) 1 ( ( ( 1

) 1 (

ln ln

σ μ

−

− +

Here membership function B ik is described by Eq (12), fuzzy inference λk is described by

Eq (13) The initial values of wμk,wσk,m ij, σijare random numbers included in (0, 1) at the

first iteration of training Reward r, threshold of evaluation errorε denoted by Eq (16) have empirical values

4 Experiments

A chaotic time series generated by Lorenz equations was used as benchmark for forecasting experiments which were MLP using BP, MLP using SGA, SOFNN using SGA Prediction precision was evaluated by the mean square error (MSE) between forecasted values and time series data

4.1 Lorenz chaos

A butterfly-like attractor generated by the three ordinary differential equations (Eq (28)) is very famous on the early stage of chaos phenomenon study (Lorenz, 1969)

)()()(

t q t p t o t q

t p t o t q t o t p

t o t p t o

ϕ φ

δ δ

Here δ φ ,ϕ are constants The chaotic time series was obtained from dimension o(t) of Eq

(29) in forecasting experiments, where Δt=0.005, δ=16.0, φ=45.92, ϕ = 4 0

+

= +

+

− Δ

−

= +

− Δ

+

= +

)) ( ) ( ) ( ( ) ( ) 1 (

)) ( ) ( ) ( ) ( ( ) ( ) 1 (

)) ( ) ( ( )

( ) 1 (

t q t p t o t t q t

q

t p t o t q t o t t p t

p

t o t p t t o t

o

ϕ φ

Fig 6 Prediction results after 2,000 iterations of training by MLP using BP

1000 1200 1400 1600 1800 2000

"MSE_BP2000.txt"

Fig 7 Prediction error (MSE) in training iteration of MLP using BP

For short-term prediction here, a three-layer MLP using BP and 3 : 6 : 1 structure shown in

Gradient constant of sigmoid function β = 1.0, discount constant α = 1.0, learning rate η= 0.01,

Fig 8 One-step ahead forecasting results by MLP using BP

10 0

5 × − The prediction results after training 2,000 times are shown in Fig 6, and the change of prediction error according to the iteration of training is shown in Fig 7 The one-step ahead prediction results are shown in Fig 8 The 500 steps MSE of one-step ahead forecasting by MLP using BP was 0.0129

4.3 Experiment of MLP using SGA

A four-layer MLP forecasting system with SGA and 3 : 60 : 2 : 1 structure shown in Fig 4

was used in experiment, and time delay τ=1 was used in embedding input space Gradient

wij α σ α μ

finish condition of training was set to 30,000 iterations where the convergence E(W) could be

observed The prediction results after 0, 5,000, 30,000 iterations of training are shown in Fig

9, Fig 10 and Fig 11 respectively The change of prediction error during training is shown in Fig 12 The one-step ahead prediction results are shown in Fig 13 The 500 steps MSE of one-step ahead forecasting by MLP using SGA was 0.0112, forecasting accuracy was 13.2% upped than MLP using BP

Fig 11 Prediction results after 30,000 iterations of training by MLP using SGA

Fig 12 Prediction error (MSE) in training iteration of MLP using SGA

( ) ( ) ( ) ( )

−

−

≤ +

− +

−

=

1 0 1 1

ˆ 4

0 4

1 0 1 1

ˆ 4

0 4

t y t

y if E

t y t

y if E

Fig 13 One-step ahead forecasting results by MLP using SGA

4.4 Experiment of SOFNN using SGA

A five-layer SOFNN forecasting system with SGA and structure shown in Fig 5 was used in

Initial value of weight wμk had random values in (0.0, 1.0), wσk=0.5 ,m ij=0.0 ,σij=15.0and

discountγ = 0.9, learning rate αmij=αwσij=αwσk=3.0×10−6 ,αwμk =2.0×10−3, the reward r was

set by Eq (31), and the finish condition of training was also set to 30,000 iterations where the

convergence E(W) could be observed The prediction results after training are shown in Fig

14, where the number of input neurons was 4 and data scale of results was modified into

(0.0, 1.0) The change of prediction error during the training is shown in Fig 15 The

one-step ahead prediction results are shown in Fig 16 The 500 one-steps MSE of one-one-step ahead

forecasting by SOFNN using SGA was 0.00048, forecasting accuracy was 95.7% and 96.3%

upped than the case by MLP using BP and by MLP using SGA respectively

( ) ( ) ( ) ( )

−

≤ +

− +

=

5 1 1 1

ˆ 5

1

5 1 1 1

ˆ 5

1

t y t

y if

t y t

y if

Fig 14 Prediction results after 30,000 iterations of training by SOFNN using SGA

Fig 15 Prediction error (MSE) in training iteration of SOFNN using SGA

sample multiplies iteration time

The 1st input neuron The 2nd input neuron The 3rd input neuron

Error of short-term prediction

Fig 16 One-step ahead forecasting results by SOFNN using SGA

Fig 17 The number of membership function neurons of SOFNN using SGA increased in training experiment

sample multiplies iteration time

the number of rule

Fig 18 The number of rules of SOFNN using SGA increased in training experiment

One advanced feature of SOFNN is its data-driven structure building The number of membership function neurons and rules increased with samples (1,000 steps in training of experiment) and iterations (30,000 times in training of experiment), which can be confirmed

by Fig 17 and Fig 18 The number of membership function neurons for the 4 input neurons was 44, 44, 44, 45 respectively, and the number of rules was 143 when the training finished

5 Conclusion

Though RL has been developed as one of the most important methods of machine learning,

it is still seldom adopted in forecasting theory and prediction systems Two kinds of neural forecasting systems using SGA learning were described in this chapter, and the experiments

of training and short-term forecasting showed their successful performances comparing with the conventional NN prediction method Though the iterations of MLP with SGA and SOFNN with SGA in training experiments took more than that of MLP with BP, both of their computation time were not more than a few minutes by a computer with 3.0GHz CPU

A problem of these RL forecasting systems is that the value of reward in SGA algorithm influences learning convergence seriously, the optimum reward should be searched experimentally for different time series Another problem of SOFNN with SGA is how to tune up initial value of deviation parameter in membership function and the threshold those were also modified by observing prediction error in training experiments In fact, when SOFNN with SGA was applied on an neural forecasting competition “NN3” where 11 time series sets were used as benchmark, it did not work sufficiently in the long-term prediction comparing with the results of other methods (Kuremoto et al, 2007; Crone & Nikolopoulos,

2007) All these problems remain to be resolved, and it is expected that RL forecasting systems will be developed remarkably in the future

Acknowledgements

We would like to thank Mr Yamamoto A and Mr Teramori N for their early work in experiments, and a part of this study was supported by MEXT-KAKENHI (15700161) and JSPS-KAKENHI (18500230)

6.References

Box, G E P & Jenkins, G (1970) Time series analysis: Forecasting and control Holden-Day,

ISBN-10 0816211043, San Francisco

Casdagli, M (1989) Nonlinear prediction of chaotic time series Physica D: Nonlinear

Phenomena Vol 35, pp 335-356

Crone, S & Nikolopoulos, K (2007) Results of the NN3 neural network forecasting

competition The 27 th International Symposium on Forecasting Program, pp 129

Engle, R F (1982) Autoregressive conditional heteroscedasticity with estimates of the

variance of U K inflation Econometrica Vol 50, pp 987-1008

Kimura, H., Yamamura, M & Kobayashi S (1996) Reinforcement learning in partially

observable Markov decision process: A stochastic gradient ascent (in Japanese)

Journal of Japanese Society for Artificial Intelligent, pp 761-768

Kimura, H & Kobayashi S (1998) Reinforcement learning for continuous action using

stochastic gradient ascent Intelligent Autonomous Systems, pp 288-295

Kodogiannis, V & Lolis, A (2002) Forecasting financial time series using neural network

and fuzzy system-based techniques Neural computing & applications Vol 11, pp

90-102

Kuremoto, T., Obayashi, M., Yamamoto, A & Kobayashi, K (2003) Predicting chaotic time

series by reinforcement learning Proceedings of the 2nd International Conference on Computational Intelligence, Robotics and Autonomous Systems (CIRAS ’03), Singapore

Kuremoto, T., Obayashi, & Kobayashi, K (2005) Nonlinear prediction by reinforcement

learning In: Lecture Notes in Computer Science, Vol 3644, pp.1085-1094, Springer,

ISBN 0302-9743 (Print) 1611-3349 (Online), Berlin

Kuremoto, T., Obayashi, & Kobayashi, K (2007) Forecasting time series by SOFNN with

reinforcement learning The 27th International Symposium on Forecasting Program,

pp 99

Lendasse, A., Oja, E., Simula, O & Verleysen, M (2007) Time series prediction competition:

The CATS benchmark Neurocomputing Vol 70, pp 2325-2329

Leung, H., Lo, T., & Wang S (2001) Prediction of noisy chaotic time series using an optimal

radial basis function IEEE Transaction on Neural Networks Vol 12, pp.1163-1172 Lorenz, E N (1963) Deterministic nonperiodic flow Journal of the atmosphere Sciences Vol

20, pp 130-141

May, R M (1976) Simple mathematical models with very complicated dynamics Nature,

Vol 261, pp 459-467

Oliveira, K A., Vannucci, A & Silva, E C (2000) Using artificial neural networks to forecast

chaotic time series Physica A Vol 284, pp 393-404

Rumelhart, D E., Hinton, G E & R J Williams, R J (1986) Learning representation by

back-propagating errors Nature Vol 232, No 9, pp 533-536

Sutton, R S & Barto, A G (1998) Reinforcement learning: an introduction The MIT Press,

ISBN 0-262-19398-1, Cambridge

Takens., F (1981) Detecting strange attractor in turbulence Lecture Notes in Mathematics,

Vol 898, pp 366-381, Springer-Verlag, Berlin

Williams., R J (1992) Simple statistical gradient following algorithms for connectionist

reinforecement learning Machine Learning, Vol 8, pp 229-256

Zhang, G P (2003) Time series forecasting using a hybrid ARIMA and neural network

model Neurocomputing, Vol 50, pp 159-175

Reinforcement Learning in System Identification

Mariela Cerrada and Jose Aguilar

Universidad de los Andes Mérida-Venezuela

1 Introduction

The Reinforcement Learning (RL) problem has been widely researched an applied in several areas (Sutton & Barto, 1998; Sutton, 1988; Singh & Sutton, 1996; Schapire & Warmuth, 1996; Tesauro, 1995; Si & Wang, 2001; Van Buijtenen et al., 1998) In dynamical environments, a learning agent gets rewards or penalties, according to its performance for learning good actions

In identification problems, information from the environment is needed in order to propose

an approximate system model, thus, RL can be used for taking the on-line information taking Off-line learning algorithms have reported suitable results in system identification (Ljung, 1997); however these results are bounded on the available data, their quality and quantity In this way, the development of on-line learning algorithms for system identification is an important contribution

In this work, it is presented an on-line learning algorithm based on RL using the Temporal Difference (TD) method, for identification purposes Here, the basic propositions of RL with

TD are used and, as a consequence, the linear TD(λ) algorithm proposed in (Sutton & Barto, 1998) is modified and adapted for systems identification and the reinforcement signal is generically defined according to the temporal difference and the identification error Thus, the main contribution of this paper is the proposition of a generic on-line identification algorithm based on RL

The proposed algorithm is applied in the parameters adjustment of a Dynamical Adaptive Fuzzy Model (DAFM) (Cerrada et al., 2002; Cerrada et al., 2005) In this case, the prediction function is a non-linear function of the fuzzy model parameters and a non-linear TD(λ) algorithm is obtained for the on-line adjustment of the DAFM parameters

In the next section the basic aspects about the RL problem and the DAFM are revised Third section is devoted to the proposed on-line learning algorithm for identification purposes The algorithm performance for time-varying non-linear systems identification is showed with an illustrative example in section fourth Finally, conclusions are presented

2 Theoretical background

2.1 Reinforcement learning and temporal differences

RL deals with the problem of learning based on trial and error in order to achieve the overall objective (Sutton & Barto, 1998) RL are related to problems where the learning agent does not know what it must do Thus, the agent must discover an action policy for maximize the

expected gain defined by the rewards that the agents gets At time t, (t=0, 1, 2, ), the agent

receives the state S t and based on this information it choises an action a t As a consequence,

the agent receives a reinforcement signal or reward r t+1 In case of the infinite time domain, a

discount weights the received reward and the discounted expected gain is defined as:

(1)

where μ, 0 < μ ≤ 1, is the discount rate, and it determines the current value of the futures

rewards

On the other hand, TD method permits to solve the prediction problem taking into account

the difference (error) between two prediction values at successive instants t and t+1, given

by a function P According to the TD method, the adjustment law for the parameter vector θ

of the prediction function P(θ) in given by the following equation (Sutton, 1988) :

(2)

where x t is a vector of available data at time t and η, 0 < η ≤ 1, is the learning rate The term

between parentheses is the temporal difference and the equation (2) is the TD algorithm that

can be used on-line in a incremental way

RL problem can be viewed as a prediction problem where the objective is the estimation of

the discounted gain defined by equation (1), by using the TD algorithm

Let be the prediction of R t Then, from equation (1):

(3)

The real value of R t+1 is not available, then, by replacing it by its estimated value in (3), the

prediction error is defined by the following equation:

(4)

which describe a temporal difference The reinforcement value r t+1 is defined in order to

obtain at time t+1 a better prediction of R t , given by , based on available information In

this manner, a good estimation in the RL problem means the optimization of R t

Thus, denoting as P and by replacing the temporal difference in (2) by that one defined in

(4), the parameters adjustment law is:

(5)

The learning agent using the equation (5) for the parameters adjustment is called

Adaptive-Heuristic-Critic (Sutton & Barto, 1998) In on-line applications, the time t is the same iteration

time in the learning process by using equation (5)

2.2 Dynamical adaptive fuzzy models

Without loss of generality, a fuzzy logic model MISO (Multiple Inputs-Single Output), is a

linguistic model defined by the following M fuzzy rules:

(6)

where x i is a vector of linguistic input on the domain of discourse U i ; y is the linguistic output variable on the domain of discourse V; F il and G l are fuzzy sets on U i and V, respectively, (i=1, ,n) and (l=1, ,M), each one defined by their membership functions

The DAFM is obtained from the previous rule base (6), by supposing input values defined

by fuzzy singleton, gaussian membership functions of the fuzzy sets defined for the fuzzy output variables and the defuzzification method given by center-average method Then, the inference mechanism provides the following model (Cerrada et al., 2005):

(7)

where X=(x 1 x 2 x n ) T is a vector of linguistic input variables x i at time t; α( v il ,t j ), β( w il ,t j ) and

γ( u l ,t j ) are time-depending functions; v il and w il are parameters associated to the variable x i

in the rule l; u l is a parameter associated to the center of the output fuzzy set in the rule l

Definition Let x i (t j ) be the value of the input variable x i to the DAFM at time t j to obtain the

output y(t j ) The generic structure of the functions αιl (v il ,t j ), βιl (w il ,t j ) and γl (u l ,t j ) in equation

(7), are defined by the following equations (Cerrada et al., 2005):

(8)

(9)

(10) where:

(11)

or

(12)

The parameters v il , w il and u l can be on-line or off-line adjusted by using the following

iterative generic algorithm:

θ

η θ

where θ(t) denotes the vector of parameters at time t, Δθ is the parameter increment at time t

and η, 0<η<1, is the learning rate Tuning algorithm by using off-line gradient-based

learning is presented in (Cerrada et al., 2002; Cerrada et al., 2005)

In this work, the initial values of parameters are randomly selected on certain interval, the

number of rules M is fixed and it is not adjusted during the learning process The input

variables x i are also known, then, the number of adjustable parameters is fixed

Clearly, by taking the functions αιl (v il ,t j ), βιl (w il ,t j ) and γl (u l ,t j ) as parameters in equation (7), a

classical Adaptive Fuzzy Model (AFM) is obtained (Wang, 1994) The mentioned parameters

are also adjusted by using the learning algorithm (13) Comparisons between the

performances of the AFM and DAMF in system identification are provided in (Cerrada et

al., 2005)

3 RL-based on-line identification algorithm

In this work, the fuzzy identification problem is solved by using the weighted identification

error as a prediction function in the RL problem, and by suitably defining the reinforcement

value according to the identification error Thus, the minimization of the prediction error (4)

drives to the minimization of the identification error

The critic (learning agent) is used in order to predict the performance on the identification as

an approximator of the system's behavior The prediction function is defined as a function of

the identification error e(t,θt )=y(t)-y e (t,θt ), where y(t) denotes the real value of the system

output at time t and y e (t,θt ) denotes the estimated value given by the identification model by

using the available values of θ at time t

Let P t be the proposed non-linear prediction function, defined as a cumulative addition on

an interval of time, given by the following equation :

k t

t k t

where e(x k ,θt )=y(k)-y e (x k ,θt ) defines the identification error at time k and the value of θ at time

t, and K defines the size of the time interval Then:

k t

t t k t

x P

),(),()(),

θ

where:

θ θ

θ θ

K t

t P x t t P x t t t e k t k t r

t

where expression in equation (15) can be viewed as the eligibility trace (Sutton & Barto, 1998),

which stores the temporal record of the identification errors weighted by the parameter λ

From (14), the function P(x t+1 ,θt ) is obtained in the following manner:

1),

K t k

k t

t k t

k t

t k t

e( , ) ( ) ( , ) 2

t k t

),(),(2

1

1

2

t t t

x

By replacing (18) into (17), the learning algorithm is given

In the prediction problem, a good estimation of R t is expected; that implies P(x t ,θt ) goes to

r t+1 + μP(x t+1 ,θt ) This condition is obtained from equation (4) Given that the prediction

function is the weighted sum of the square identification error e 2 (t), then it is expected that:

) , ( ) , 1 (

On the other hand, a suitable adjustment of identification model means that the following

condition is accomplished:

),(),1(

The reinforcement r t+1 is defined in order to accomplish the expected condition (19) and

taking into account the condition (20) Then, by using equations (14) and (18), the

reinforcement signal is defined as:

) ,θ P(x ) ,θ P(x t

x e

2

1 ( ) if 2

1

θ

) ,θ P(x ) ,θ P(x

rt+ 1= 0 if t+ 1 t ≤ t t (22)

In this way, the identification error into the prediction function P(x t+1 ,θt ), according to the

equation (18), is rejected by using the reinforcement in equation (22) The learning rate η in

(17) is defined by the following equation:

10

; )1()1()

−+

−

ηρ

ηη

k

k

Thus, an accurate adjustment of parameters is expected Usually, η(0) is around 1, and ρ is

around 0 Parameters μ and λ can depend on the system dynamic: small values in case of

slow dynamical systems, and values around 1 in case of fast dynamical systems

In this work, the proposed RL-based algorithm is applied to fuzzy identification and the

identification model is provided by the DAFM in (7) Then, the prediction function P is a

obtained

3.1 Descent-gradient-based analysis

The proposed identification learning algorithm can be studied like a descent-gradient

method with respect to the parametric predictive function P In the descent-gradient method

for optimization, the objective is to find the minimal value of the error measure on the

where E{z|x} is the expected value of the real value z, from the knowledge of the available

data x

In this work, the learning algorithm (17) is like a learning algorithm (24), based on the

descent-gradient method, where r t+1 +μP(x t+1 , θt ) is the expected value E{z|x} in (25) By

appropriate selecting r t+1 according to (21) and (22), the expected value in the learning

problem is defined in two ways:

Then, the parameters adjustment is made on each iteration in order to attain the expected

value of the prediction function P according to the predicted value of P(x t+1 ,θt ) and the real

value P(x t,θt ) In both of cases, the expected value is minor than the obtained real value

P(x t,θt) and the selected value of r t+1 defines the magnitude of the defined error measure

4 Illustrative example

This section shows an illustrative example applied to fuzzy identification of time-varying

non-linear systems by using the proposed on-line RL-based identification algorithm and the

DAFM described in section 2.2 Comparisons by using off-line gradient-based tuning

algorithm are presented in order to highlight the algorithm performance For off-line

adjustment purposes, the input-output training data is obtained from Pseudo-Random

Binary Signal (PRBS) input signal The performance of the fuzzy identification is evaluated

according to the identification relative error (e r =(y(t)-y e (t))/y(t)) normalized on [0,1]

The system is described by the following difference equation:

)()]

1(),([)1

where

) k/

sen(

a(k)

k y k y

k a k y k y k y k y k y g

250255

)1()(1

)]

()()[

1()()]

1(),(

π+

=

−++

+

−

=

In this case, the unknown function g=[.] is estimated by using the DAFM and, additionally, a

sudden change on a(k) is proposed by setting a(k)=0, k>400 After an extensive training

phase, the fuzzy model with M=8, δ1=4 and δ2=1 (in equations (8),(9),(11)), has been chosen

In this case, the fuzzy identification performance is adequate and the Root Mean Square

Error (RMSE) is 0.1285 in validation phase Figure 1 shows the performance of the DAFM

using the off-line gradient-based tuning algorithm with initial conditions on the interval

[0,1] and using the following input signal:

500

y 1001 )25/2()

(

k k

sen k

sen

k k

k sen k

u

ππ

Fig 1 Fuzzy identification using off-line tuning algorithm and DAFM

In the following, fuzzy identification performance by using the DAFM with the proposed RL-based tuning algorithm is presented Equation (17) is used for the parameters adjustment with the prediction function defined in (14) and the reinforcement defined in (21)-(22) Here, λ=μ=0.9, K=5 and the learning rate is set up by the equation (23) with ρ=0.01 Note that the iteration index t is the same time k in system (28) After experimental proofs,

the performance approaching the accuracy obtained from off-line adjustment is obtained

with M=6 and initial conditions on [0.5,1.5] Here, the RMSE= 0.0838 is achieved Figure 2 shows the tuning algorithm performance and table 1 shows the comparative values related

Table 1 Comparison between the on-line proposed algorithm and off-line tuning

Fig 2 Fuzzy identification using RL-based tuning algorithm and DAFM

4.1 Initial condition dependence

In order to show the algorithm sensibility according to the initial conditions of the fuzzy

model parameters, the following figures show the tuning algorithm performance In this

case, the system is described by the equation (31):

) k/

sen(

a(k)

k y k

y k a

k u k

y k u k y k y k y k y

10021.01

)1()2()(

)()1)2()(

1()2()1()()1

π+

=

−+

−+

1000801

and 5001 )250/2()(

k k

sen k

sen

k k

k sen k u

ππ

Figure 3 shows the tuning process by using a model with M=20 and initial conditions on the

interval [0.5,1.5] In this case, even when the initial error is large, the tuning algorithm

performance also shows an adequate performance and the tuning process has an suitable

evolution (here, a sudden change on a(k) is not considered) Figure 4 shows the tuning

process by using a model with initial conditions on the interval [0,1] an also a suitable

performance of the proposed identification algorithm is shown

Fig 3 Fuzzy identification using RL-based tuning algorithm and DAFM with initial

conditions on [0.5,1.5].

The previous tests show the performance and the sensibility of the proposed on-line algorithm is adequate in terms of (a) The initial conditions of the DAFM parameters, (b)

Changes on the internal dynamic (the term a(k) in the example) and (c) Changes on the inputs signal (the proposed input u(k))

These ones are very important aspects to be evaluated in order to consider an on-line identification algorithm In the example, even though the initial error depends on the initial conditions of the DAFM parameters, a good evolution of the learning algorithm is

accomplished Table 1 also shows the number of rules M do not strongly determines the

global performance of the proposed on-line algorithm although a similar RMSE could be obtained with a low number of rules and off-line tuning However, this one could be not reached whether good quality and quantity of historical data is not available in off-line approaches

Fig 4 Fuzzy identification using RL-based tuning algorithm and DAFM with initial

conditions on [0,1]