Controlling Hyperchaotic Finance System with Combining Passive and Feedback Controllers

In this paper, a novel control method that combines passive, linear feedback, and dislocated feedback control methods is proposed and applied to the control of the four-dimensional hyperchaotic finance system which has been introduced and controlled with the linear feedback and speed feedback control methods by Yu, Cai, and Li (2012). The stability of the hyperchaotic finance system at its equilibrium points is ensured on the basis of a Lyapunov function. Computer simulations are used for verifying all the theoretical analyses visually. In the simulations, the proposed control method is also compared with the speed feedback and linear feedback control methods to observe its effectiveness. Finally, the comparative findings are discussed

Landmark Based Reward Shaping in Reinforcement Learning with Hidden States

While most of the work on reward shaping focuses on fully observable problems, there are very few studies that couple reward shaping with partial observability. Moreover, for problems with hidden states, where there is no prior information about the underlying states, reward shaping opportunities are unexplored. In this paper, we show that landmarks can be used to shape the rewards in reinforcement learning with hidden states. Proposed approach is empirically shown to improve the learning performance in terms of speed and quality

A History Tree Heuristic to Generate Better Initiation Sets for Options in Reinforcement Learning

Options framework is a prominent way to improve learning speed by means of temporally extended actions, called options. Although various attempts focusing on how to derive high quality termination conditions for options exist, the impact of initiation set generation of an option is relatively unexplored. In this work, we propose an effective heuristic method to derive useful initiation set elements via an analysis of the recent history of events

A Concept Filtering Approach for Diverse Density to Discover Subgoals in Reinforcement Learning

In the reinforcement learning context, subgoal discovery methods aim to find bottlenecks in problem state space so that the problem can naturally be decomposed into smaller subproblems. In this paper, we propose a concept filtering method that extends an existing subgoal discovery method, namely diverse density, to be used for both fully and partially observable RL problems. The proposed method is successful in discovering useful subgoals with the help of multiple instance learning. Compared to the original algorithm, the resulting approach runs significantly faster without sacrificing the solution quality. Moreover, it can effectively be employed to find observational bottlenecks of problems with perceptually aliased states

Automatic landmark discovery for learning agents under partial observability

In the reinforcement learning context, a landmark is a compact information which uniquely couples a state, for problems with hidden states. Landmarks are shown to support finding good memoryless policies for Partially Observable Markov Decision Processes (POMDP) which contain at least one landmark. SarsaLandmark, as an adaptation of Sarsa(lambda), is known to promise a better learning performance with the assumption that all landmarks of the problem are known in advance

Local Roots A Tree Based Subgoal Discovery Method to Accelerate Reinforcement Learning

Subgoal discovery in reinforcement learning is an effective way of partitioning a problem domain with large state space. Recent research mainly focuses on automatic identification of such subgoals during learning, making use of state transition information gathered during exploration. Mostly based on the options framework, an identified subgoal leads the learning agent to an intermediate region which is known to be useful on the way to goal. In this paper, we propose a novel automatic subgoal discovery method which is based on analysis of predicted shortcut history segments derived from experience, which are then used to generate useful options to speed up learning. Compared to similar existing methods, it performs significantly better in terms of time complexity and usefulness of the subgoals identified, without sacrificing solution quality. The effectiveness of the method is empirically shown via experimentation on various benchmark problems compared to well known subgoal identification methods

GENERATING EFFECTIVE INITIATION SETS FOR SUBGOAL-DRIVEN OPTIONS

Options framework is one of the prominent models serving as a basis to improve learning speed by means of temporal abstractions. An option is mainly composed of three elements: initiation set, option's local policy and termination condition. Although various attempts exist that focus on how to derive high-quality termination conditions for a given problem, the impact of initiation set generation is relatively unexplored. In this work, we propose an effective goal-oriented heuristic method to derive useful initiation set elements via an analysis of the recent history of events. Effectiveness of the method is experimented on various benchmark problems, and the results are discussed

Synchronization and control of chaos in supply chain management

This paper presents the synchronization and control of a chaotic supply chain management system based on its mathematical model. For this purpose, active controllers are applied for the synchronization of two identical chaotic supply chain management systems. Also, linear feedback controllers are designed and added to the nonlinear supply chain management system to achieve the control of the system. In these methods, synchronization and control are established by using Lyapunov stability theory. As a result, the synchronization and control of chaotic supply chain management system are realized numerically. Computer simulations are performed to verify the robustness of proposed synchronization and control methods. (C) 2014 Elsevier Ltd. All rights reserved

Control and synchronization of chaotic supply chains using intelligent approaches

This paper presents the control of chaotic supply chain with Artificial Neural Network (ANN) based controllers and the synchronization of two identical chaotic supply chains that have different initial conditions with Adaptive Neuro-Fuzzy Inference System (ANFIS) based controllers. A hybrid intelligent control model is designed in which the linear feedback and active control signals are also used for achieving the control and synchronization, respectively. ANN and ANFIS controllers are trained according to the model. Thereby, the advantages of classical and intelligent control methods are combined. Computer simulations show that the proposed approach is very effective for the control and synchronization of chaos in supply chain systems. (C) 2016 Elsevier Ltd. All rights reserved

Synchronization of Chaos in Nonlinear Finance System by means of Sliding Mode and Passive Control Methods: A Comparative Study

In this paper, two different control methods, namely sliding mode control and passive control, are investigated for the synchronization of two identical chaotic finance systems with different initial conditions. Based on the sliding mode control theory, a sliding surface is determined. A Lyapunov function is used to prove that the passive controller provides global asymptotic stability of the system. Numerical simulations validate the synchronization of chaotic finance systems with the proposed sliding mode and passive control methods. The synchronization performance of these two methods is compared and discussed