International Journal of Computing and Digital SystemsISSN (2210-142X)Int. J. Com. Dig. Sys. 4, No.2 ierarchical Reinforcement Learning: A SurveyMostafa Al-EmranAdmission & Registration Department, Al-Buraimi, OmanReceived 29 Dec. 2014, Revised 7 Feb. 2015, Accepted 7 Mar. 2015, Published 1 Apr. 2015Abstract: Reinforcement Learning (RL) has been an interesting research area in Machine Learning and AI. HierarchicalReinforcement Learning (HRL) that decomposes the RL problem into sub-problems where solving each of which will be morepowerful than solving the entire problem will be our concern in this paper. A review of the state-of-the-art of HRL has beeninvestigated. Different HRL-based domains have been highlighted. Different problems in such different domains along with someproposed solutions have been addressed. It has been observed that HRL has not yet been surveyed in the current existing research;the reason that motivated us to work on this paper. Concluding remarks are presented. Some ideas have been emerged during thework on this research and have been proposed for pursuing a future research.Keywords: Reinforcement Learning; Hierarchical Reinforcement Learning; Q-learning.1.INTRODUCTIONReinforcement Learning (RL) has been an interestingresearch field in the community of Machine Learning andAI that received many attentions from the fields ofoperations research due to its self-adaptation and selflearning . RL algorithms work on maximizing theagent learning while interacting with its environmentdirectly . Hierarchical Reinforcement Learning (HRL)works on decomposing the RL problem into sub-problemswhere solving each of which will be more powerful thansolving the entire problem . In the recent years, , and  stated that the problem of “Curse ofDimensionality” (which is the exponential growth ofmemory requirements with the number of state variables)has been solved via HRL. The RL then works on reducingdimensionality through decomposing it into several levels.HRL helps to overcome the agent-learning complexitiesthat are considered as one of the typical issues in thelearning environments . Different HRL-baseddomains have been investigated within this work.Different problems in such different domains along withsome proposed solutions have been addressed.The paper is organized as follows: section 2demonstrates a background on RL, HRL and Q-learning.Section 3 addresses the main contribution in the area ofHRL. Section 4 presents the conclusion and the ideas thathave been emerged while conducting this research.2.BACKGROUNDA. Reinforcement LearningReinforcement learning (RL) is one of the machinelearning areas in which an agent has to interact with itsenvironment in order to achieve a goal (as in Figure 1).RL based on the structure of Markov Decision Processes(MDPs); a reliable structure for the agent learning whileinteracting with its environment in order to receiverewards and drawbacks , , . The essentialelements of RL are the states, actions and reinforcements. Via the agent’s sensors, the agent recognizes theenvironment and implements actions (according to apolicy) in which leads to changes in the environment.According to these changes, the agent obtains rewardsbased on the taken actions , . RL improves strategythrough the learning via trial and error by interacting withthe environment and recognize the best actions at eachstate in order to reach the goal and gain the best rewards, . RL tries to find the best policy that increases thetotal reward.  indicated that RL algorithms work onhow the agent can learn to estimate an optimal strategywhile to interact with its environment.Figure 1. Reinforcement Learning Basic Model. B. Hierarchical Reinforcement LearningHierarchical Reinforcement Learning (HRL) refers tothe notion in which RL problem is decomposed into subproblems (sub-tasks) where solving each of which will bemore powerful than solving the entire problem , , and , .  and  has defined HRL as the set ofE-mail: [email protected]://journals.uob.edu.bh
138Mostafa Al-Emran: Hierarchical Reinforcement Learning: A Surveycomputational techniques that enlarge RL procedures inorder to involve temporarily abstract actions. Thehierarchical decomposition has some advantages such as:reducing the sub-problem’s computational complexity,managing the sub-problems individually will maximize itsreusability which in turn will speed up the learningprocess . HRL techniques use several forms ofabstractions that have the ability to handle theexponentially increasing number of parameters which arerequired to be learned specifically in big problems foreffectively reducing the search space which in turn allowsthe agent to determine the optimal solution . HRL isone of the well-known methods to resolve the problem of“Curse of Dimensionality” .  stated that a welldesigned reward function along with HRL can decreasethe number of impractical acts of exploration which inturn allows the agent to interact easily and quickly withthe environment.  presented that utterance planningand content selection in Natural Language Generation(NLG) can be optimized via HRL along with BayesianNetworks. Various HRL models are available likeMAXQ, Hierarchical abstract machines (HAMs), ALispand options , ; the models that scale RL to largestate spaces problems by decomposing them into subproblems.C. Q-LearningQ-learning is one of the RL algorithms that has beensuccessfully used in many domains such as: facerecognition, simple toys, web-based education and manyothers . Q-learning tries to find an optimal actionpolicy by estimating the optimal state-action functionQ(s, a) where s state from the set of the possible statesS, a action from the set of the possible actions A. TheQ function described the maximum reward achievedwhen an action a is executed over the state s . The Qlearning equation is described as follows:Where refers to the learning rate, refers to thediscount factor and r refers to the reward of executing theaction a over the state s.3.TECHNICAL PARTDifferent domains with different problems based onHRL will be discussed and explored within this section:A. HRL Based Control Architecture for rban search and rescue (USAR) scenes are clutteredenough and the information about such environment isalready unknown due to their desolation. Thereforesearching for victims in such environments using humanteleoperation of rescue robots is a difficult task. Differentsolutions have been proposed to resolve the USARproblem as in the following table:TABLE itectureTECHNIQUES USED FOR RESOLVING USAR PROBLEM.DescriptionWhere the communication between thehuman and robot will be lost due to theenvironment nature . Hence thistechnique makes the search task verydifficult for the robot.Since this technique is fully robot-based;humans couldn’t trust the robot in suchcritical tasks. Moreover, using thistechnique is challenging due to the factthat dust, debris in such environments willaffect the sensors, hence this techniqueremains ineffective and needs moreimprovements.Anotheralternativetechnique by .HRL algorithm enables the robot to learnand make his own decisions based on therescue tasks, victim identification andexploration by performing these tasks veryquickly and efficiently. The experimentsrevealed the effectiveness of the proposedtechnique by observing the ability of therobot while exploring the whole USARenvironment .B. HRL in Computer GamesComputer games are one of the hot topics for researchin AI and machine learning. One of the issues in computergames that attract researchers to work on is the NonPlayer Characters (NPCs) behaviors due to theircomplexity and difficulty in to be represented by typicalfinite state machines. The control details of NPCs at allstages are commonly hand-coded; the reason that makesthe development task consumes more time and exposedfor errors. To overcome these limitations, HRL has beenused based on the Hierarchies of Abstract Machines(HAMs). Through applying the proposed solution, systemdesigners can determine points within the program itselfwhere they don’t care about how the code will be writtenwhile it’s determined through the robot’s learning process.Experiments have been conducted to test the efficiency ofthe proposed solution under the Quake2UR (whichperforms as 3D Game Server) and ALisp system (whichperforms as a client). Results revealed that the proposedsolution was very flexible and satisfies the need forcontrolling NPCs easily .Similarly,  has proposed the MaxQ-Q HRLalgorithm in the NPCs in order to enhance the user’sexperience and to improve the natural humanness whileinteracting with computer games. Experiments wereperformed using “Capture the Flag” strategic game. Acomparison was made between the NPCs based on FiniteState Machines (FSM) and the NPCs based on MaxQ-Qthrough the game. Results indicated that NPCs based onMaxQ-Q HRL are 52% much better than NPCs based onFSM.http://journals.uob.edu.bh
Int. J. Com. Dig. Sys. 4, No.2, 137-143 (Apr-2015)Moreover, the Infinite Mario game is one of theinteresting action games that got popularity in the area ofAI and machine learning. The domain of this game is verycomplex and contains huge state-action spaces.  hasintegrated the HRL along with the object-orientedrepresentation in order to reduce the state-action spaces inthe game domain. Accordingly, involving HRL hasincreased the agent-learning performance.C. Course-Scheduling Algorithm of Option-based HRLTraditional timetable scheduling system implementsRL algorithm. Whereas this algorithm suffers from theoscillation period due to the reason that the reward of RLalgorithm is not immediately obtained. This will affectthe RL algorithm to indicate that the timetable statedimension is extremely large while scheduling thecourse.  proposed applying an option-based HRLalgorithm to the timetable scheduling strategy in order toenhance the performance of traditional RL. The Q-valueupdate of option-based HRL algorithm is as follows:Where r denotes the reward, denotes the discount ratefactor, a denotes the learning rate and t is the time that theoption takes. The environment parameters are theinstructor, course, college, major, semester, grade andclassroom indicating that the agent has no priorknowledge about the environment before learning.Experiments results revealed that the proposed algorithmhas the ability to reduce the oscillation period. Moreover,while HRL is involved in this algorithm, the coursescheduling actions are divided into sub-tasks; this willallow the agent to learn quickly and select the optimalstrategy. Furthermore, the results demonstrated that the Qvalue update equation is much smoother than the regularQ-learning algorithm.D. HRL Approach for Motion Planning in MobileRoboticsMotion planning task is one of the interesting tasks inmobile robotics that looks for generating a free-collisionpath from the initial point to the goal point for the robot. applied RL in order to avoid all the obstacles inmobile robotics through the use of Neural Networks,However this became an old technique where proposed an option-based HRL in which basic behaviorsare used. Each behavior is individually learned in thelearning process; this allows the robot to organize all thebasic behaviors to solve the problem of the motionplanning. Semi-Markov Q-learning has been used toestimate the state-option function values Q(s, o) viachoosing an option o in the state s based on the policy μ.After implementing o, the final state s’ along with the Qvalue will be updated based on the equation:139Where denotes the discount rate, α denotes the learningrate and k refers to number of steps between s and s’.Experimental tests have been implemented in simulation.Results revealed that the proposed algorithm has theability to work effectively in unknown environments aswell as to avoid all the encountered obstacles by the robotin the motion planning task without the use of NeuralNetworks.E. HRL using Path Clustering intended to resolve small and medium scales RLproblems through the use of path clustering in order toenable its hierarchical decomposition. Moreover, how toenhance the Q-learning algorithm performance viaautomatically finding sub-goals and making better usageof the knowledge acquired. HRL path clustering methodhas been proposed which allows the robot to acquire theknowledge about the states’ sequences which lead to thegoal and introduce those states at the end of the sequencesas sub-goals. Taxi-problem (as one of the standards in RLand is being used for testing the HRL solutions) has beenused. In this problem, sub-goals enhance the learningspeed by achieving good results faster than the traditionalQ-learning due to the fact that the problem scale is verysmall. It has been proposed to insert the sub-goals into thelearning process. Results revealed that the earlyinvolvement of sub-goals will achieve a sub-optimallearning.F. Web Service Composition method using HRLWeb services composition facilitates the combinationof single web-services into featured/valued services thatcould satisfy the users’ needs while the individual singleweb-services couldn’t so. The dynamic web servicecomposition model is shown in Figure 2. When the “taskacceptor” of the “service agency i” receives the data, thecorresponding flow chart will be generated through the“composed service engine”. Accordingly, each simpleservice will be executed by the “business executionengine”. “Service agency i” will receive the results andthe system continues to invoke “service agency j” thatdelivers other services .http://journals.uob.edu.bh
140Mostafa Al-Emran: Hierarchical Reinforcement Learning: A SurveyFigure 2. Dynamic Web Service Composition Model. One of the main problems of the dynamic web-servicecomposition is the optimization problem (i.e. how to findan optimal policy). Different solutions have beenproposed to accomplish an optimal policy for dynamicweb service composition.  proposed an algorithmbased on RL; however this algorithm suffers from the“Curse of dimensionality” specifically in large-scaleproblems of web-service composition. On the other hand, introduces the HRL, a continuous time integratedMAXQ algorithm to handle the large-scale problems inthe context of Semi-Markov decision process (SMDP).This algorithm has been compared to the Q-learningalgorithm. Simulation results revealed that theperformance of MAXQ algorithm is much better than theQ-learning algorithm by comparing the both with adiscount factor a 0.01 due to the reason that the MAXQalgorithm has the ability to accelerate the learning speed.Moreover, by comparing both algorithms with differentnumber of tasks, it has been observed that as much as thenumber of tasks is increasing, the success rate of the Qlearning decreases much faster than MAXQ.  provedthat the proposed algorithm is much better than the Qlearning algorithm to handle the problem of Curse ofdimensionality in large-scale problems of web-servicecomposition.Another problem in dynamic web-service composition isthat how to combine a collection of simple web-servicesbased on the users’ functional needs and how to choosesuch services based on users’ QoS needs among all theavailable services.  proposed an algorithm based onHRL and Logic of Preference; the algorithm thatefficiently deals with both users’ functional and QoSneeds and has the ability to work in large-scale problems.The algorithm is decomposed into two parts: MAXQ (forservice composition) and Logic of Preference (forchoosing the service).An experiment has been conducted using 500 webservices and 180 states. Experiments results revealed thatthe computation cost is significantly decreasing as longas the number of execution times is increasing.Moreover, results showed that utilizing HRL caneffectively speed up the composition task.G. A Combined HRL Based Approach for Multi-robotCooperative Target Searching in Complex knownenvironments is one of the fundamentals in variousapplications like target searching and exploring theenvironments. One of the main weaknesses is that thelearning ability in many RL approaches is temporary dueto the reason that it is environment-based; the ability todeal with new environments and specifically dynamicenvironments.  suggested a combination of bothOption and MAXQ algorithms in which the knowledgeand the hierarchical structure are introduced andconstructed respectively by both algorithms. However,this solution still lacks the exploration of the unnecessaryparts of the environments.  proposed an effectiveHRL algorithm that combines both the MAXQ andOption algorithms (as in Figure 3) where all the requiredparameters will be automatically obtained through thelearning, unlike other algorithms that selects parametersvia trial and error. The proposed solution has the abilityto evaluate the feedback and tries to get featuredparameters for future processes; the reason that makesthis solution is unique for such environments ascomparing with the others. The simulation resultsrevealed that the proposed solution has the ability toallow a team of robots to collaboratively achieve targetsearching in unknown environments.http://journals.uob.edu.bh
Int. J. Com. Dig. Sys. 4, No.2, 137-143 (Apr-2015)4.Figure 3. MAXQ and Option algorithms combination. H. Deep Belief Network for Modeling HRL policiesIntelligent robots faced multiple tasks during theirlifetime that requires concurrent modeling and involvescontrolling the complexity in unknown environments.Policy learning is one of the major issues that suffersfrom the “Curse of Dimensionality” which leads toscaling problems for regular RL. To handle this issue, therobot should efficiently acquire and reuse potentialknowledge.  proposed a novel learning technique forHRL based on Conditional Restricted BoltzmannMachines (CRBMs) to tackle the growing learning andscaling problems for regular RL. A simple Taxi domainwas designed to investigate the learning capabilities andrepresent the HRL policies. The designed taxi domainrepresents a car in one dimensional space that picks apacket from a state and drops it at a destination asdemonstrated in Figure 4. HRL based-CRBMs havepotential to offer a uniform means to concurrently learnpolicies and associate abstract state features within areliable network structure.141CONCLUSION & FUTURE WORKReinforcement Learning (RL) plays a significant role inthe area of Machine Learning and AI. HRL focuses ondecomposing the RL problems into sub-problems wheresolving each sub-problem independently is much easierand powerful than solving the entire problem. A reviewof the state-of-the-art of Hierarchical ReinforcementLearning has been investigated. Different research areaswith different problems based on HRL have beensurveyed within this paper such as: rescue robots incluttered environments, computer games, coursescheduling, motion planning in mobile robotics, webservice composition, path clustering, multi-robotcooperation and intelligent robots.While working on this survey, some ideas have beenemerged and can be pursued as a future work; the ideasthat require more attention from researchers who areinterested in the HRL field. Such ideas could besummarized as per the following: Will multi-robot cooperation be an effective wayto search for victims in cluttered USARenvironments? As to compare with the proposedsolutions by , . Moreover, these nts so further research needs to focus onlarge-scale environments to test their efficiency. How multi-robot cooperation will support theproblems of web-service compositions? As tocompare with .  focused on smaller discrete RL problemsthrough the use of path clustering. Further researchmay focus on larger continues RL problems.  compares both MAXQ and Q-learningalgorithms in the area of web-servicecompositions. Further work could compare the twoalgorithms in robots race and see which one isgoing to learn and reach the goal state faster?REFERENCESBuitrago-Martinez, A., Rosa, R., & Lozano-Martinez, F. (2013,October). Hierarchical Reinforcement Learning Approach forMotion Planning in Mobile Robotics. In Robotics Symposium andCompetition (LARS/LARC), 2013 Latin American, pp. 83-88.IEEE.Dayan, P., & Niv, Y. (2008). Reinforcement learning: the good,the bad and the ugly. Current opinion in neurobiology, 18(2), pp.185-196.Ming, G. F., & Hua, S. (2010). Course-scheduling algorithm ofoption-based hierarchical reinforcement learning. In 2010 SecondInternational Workshop on Education Technology and ComputerScience, Vol. 1, pp. 288-291.Gil, P., & Nunes, L. (2013, June). Hierarchical reinforcementlearning using path clustering. In Information Systems andTechnologies (CISTI), 2013 8th Iberian Conference on (pp. 1-6).IEEE.Stulp, F., & Schaal, S. (2011, October). Hierarchicalreinforcement learning with movement primitives. In HumanoidRobots (Humanoids), 2011 11th IEEE-RAS InternationalConference on (pp. 231-238). IEEE.Figure 4. Simplified Taxi Domain. http://journals.uob.edu.bh
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Int. J. Com. Dig. Sys. 4, No.2, 137-143 (Apr-2015)143Mostafa Al-Emran is the Head ofTechnical Support / Admission &Registration Department at AlBuraimi University College. AlEmran got his BSc in ComputerSciencefromAlBuraimiUniversity College with the firsthonor level. He got his MSc inInformatics from The BritishUniversity in Dubai with adistinction level. Al-Emran haspublished some research papers and is currently working ondifferent research areas in Computer Science.http://journals.uob.edu.bh
Figure 1. Reinforcement Learning Basic Model.  B. Hierarchical Reinforcement Learning Hierarchical Reinforcement Learning (HRL) refers to the notion in which RL problem is decomposed into sub-problems (sub-tasks) where solving each of which will be more powerful than solving the entire problem , ,  and , .