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16 篇 会议

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检索条件"任意字段=2013 4th IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning, ADPRL 2013"

共 16 条记录，以下是1-10 订阅

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Exploring the Relationship of Reward and Punishment in reinforcement learning Evolving Action Meta-learning Functions in Goal Navigation

Exploring the Relationship of Reward and Punishment in Reinf...

引用

4th ieee International symposium on adaptive dynamic programming and reinforcement learning (adprl)

作者： Lowe, Robert Ziemke, Tom Univ Skovde Interact Lab Skovde Sweden

ISBN: (纸本)9781467359252

We present a reinforcement learning algorithm based on Dyna-Sarsa that utilizes separate representations of reward and punishment when guiding state-action value learning and action selection. the adoption of policy meta-learning optimized by a genetic algorithm is explored and results in the context of a two-armed bandit goal-navigation task in a simple grid world are presented. the findings argue for an important role for a genetic algorithm approach for constructing the foundations of autonomous reinforcement learning agents.

关键词： Value Reward Punishment reinforcement Contingencies SARSA TD learning Genetic Algorithm

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adaptive Optimal Control for Nonlinear Discrete-Time Systems

Adaptive Optimal Control for Nonlinear Discrete-Time Systems

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4th ieee International symposium on adaptive dynamic programming and reinforcement learning (adprl)

作者： Qin, Chunbin Zhang, Huaguang Luo, Yanhong Northeastern Univ Sch Informat Sci & Engn Shenyang 110004 Peoples R China

ISBN: (纸本)9781467359252

this paper proposes an on-line near-optimal control scheme based on capabilities of neural networks (NNs), in function approximation, to attain the on-line solution of optimal control problem for nonlinear discrete-time systems. First, to solve the Hamilton-Jacobi-Bellman (HJB) equation forward-intime appearing in the optimal control problem, two neural networks are used to approximate the cost function and to compute the optimal control policy, respectively. And then, according to the Bellman's optimality principle and the adaptive technology, the on-line weight updating laws for the critic network and action network are derived, respectively. Further, considering NNs approximative errors, the stability analysis of the closed-loop system is demonstrated by Lyapunov theory. At last, a numerical example is provided to demonstrate the effectiveness of the proposed method.

关键词： adaptive optimal control Hamilton-Jacobi-Bellman equation neural network adaptive dynamic programming

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Optimal control for a class of nonlinear systems with state delay based on adaptive dynamic programming with ε-error bound

Optimal control for a class of nonlinear systems with state ...

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4th ieee International symposium on adaptive dynamic programming and reinforcement learning (adprl)

作者： Lin, Xiaofeng Cao, Nuyun Lin, Yuzhang Guangxi Univ Sch Elect Engn Nanning 530004 Peoples R China Tsinghua Univ Dept Elect Engn Beijing Peoples R China

ISBN: (纸本)9781467359252

In this paper, a finite-horizon epsilon-optimal control for a class of nonlinear systems with state delay is proposed by adaptive dynamic programming (ADP) algorithm. First of all, the performance index function is defined and the Hamilton-Jacobi-Bellman (HJB) equation is obtained for the problem, the convergence of the iterative algorithm is also presented. then, ADP algorithm for finite-horizon optimal control is introduced with an epsilon-error bound so as to get the epsilon-optimal control, and BP neural network is used to implement ADP algorithm. At last, an example is given to demonstrate the effectiveness of the proposed algorithm.

关键词： adaptive dynamic programming state delay epsilon-optimal control finite time nonlinear systems

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Finite Horizon Stochastic Optimal Control of Uncertain Linear Networked Control System

Finite Horizon Stochastic Optimal Control of Uncertain Linea...

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4th ieee International symposium on adaptive dynamic programming and reinforcement learning (adprl)

作者： Xu, Hao Jagannathan, S. Missouri Univ Sci & Technol Dept Elect & Comp Engn Rolla MO 65409 USA

ISBN: (纸本)9781467359252

In this paper, finite horizon stochastic optimal control issue has been studied for linear networked control system (LNCS) in the presence of network imperfections such as network-induced delays and packet losses by using adaptive dynamic programming (ADP) approach. Due to an uncertainty in system dynamics resulting from network imperfections, the stochastic optimal control design uses a novel adaptive estimator (AE) to solve the optimal regulation of uncertain LNCS in a forward-in-time manner in contrast with backward-in-time Riccati equation-based optimal control with known system dynamics. Tuning law for unknown parameters of AE has been derived. Lyapunov theory is used to show that all the signals are uniformly ultimately bounded (UUB) with ultimate bounds being a function of initial values and final time. In addition, the estimated control input converges to optimal control input within finite horizon. Simulation results are included to show the effectiveness of the proposed scheme.

关键词： Networked Control System adaptive dynamics programming and reinforcement learning Finite horizon Stochastic Optimal Control adaptive Estimator

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A reinforcement learning algorithm developed to model GenCo strategic bidding behavior in multidimensional and continuous state and action spaces

A reinforcement learning algorithm developed to model GenCo ...

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4th ieee International symposium on adaptive dynamic programming and reinforcement learning (adprl)

作者： Lau, Alfred Yong Fu Srinivasan, Dipti Reindl, thomas Natl Univ Singapore Dept Elect Comp Engn 4 Engn Dr 3 Singapore 117576 Singapore Natl Univ Singapore Solar Energy Res Inst Singapore 117574 Singapore

ISBN: (纸本)9781467359252

the electricity market have provided a complex economic environment, and consequently have increased the requirement for advancement of learning methods. In the agent-based modeling and simulation framework of this economic system, the generation company's decision-making is modeled using reinforcement learning. Existing learning methods that models the generation company's strategic bidding behavior are not adapted to the non-stationary and non-Markovian environment involving multidimensional and continuous state and action spaces. this paper proposes a reinforcement learning method to overcome these limitations. the proposed method discovers the input space structure through the self-organizing map, exploits learned experience through Roth-Erev reinforcement learning and the explores through the actor critic map. Simulation results from experiments show that the proposed method outperforms Simulated Annealing Q-learning and Variant Roth-Erev reinforcement learning. the proposed method is a step towards more realistic agent learning in Agent-based Computational Economics.

关键词： reinforcement learning strategic bidding behavior agent-based modeling electricity market

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Scalarized Multi-Objective reinforcement learning: Novel Design Techniques

Scalarized Multi-Objective Reinforcement Learning: Novel Des...

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4th ieee International symposium on adaptive dynamic programming and reinforcement learning (adprl)

作者： Van Moffaert, Kristof Drugan, Madalina M. Nowe, Ann Vrije Univ Brussel Dept Comp Sci B-1050 Brussels Belgium

ISBN: (纸本)9781467359252

In multi-objective problems, it is key to find compromising solutions that balance different objectives. the linear scalarization function is often utilized to translate the multi-objective nature of a problem into a standard, single-objective problem. Generally, it is noted that such as linear combination can only find solutions in convex areas of the Pareto front, therefore making the method inapplicable in situations where the shape of the front is not known beforehand, as is often the case. We propose a non-linear scalarization function, called the Chebyshev scalarization function, as a basis for action selection strategies in multi-objective reinforcement learning. the Chebyshev scalarization method overcomes the flaws of the linear scalarization function as it can (i) discover Pareto optimal solutions regardless of the shape of the front, i. e. convex as well as non-convex, (ii) obtain a better spread amongst the set of Pareto optimal solutions and (iii) is not particularly dependent on the actual weights used.

关键词： design techniques learning (artificial intelligence) optimal solution Convexity flaws learning Linear Combination Solutions

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A Combined Hierarchical reinforcement learning Based Approach For Multi-robot Cooperative Target Searching in Complex Unknown Environments

A Combined Hierarchical Reinforcement Learning Based Approac...

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4th ieee International symposium on adaptive dynamic programming and reinforcement learning (adprl)

作者： Cai, Yifan Yang, Simon X. Xu, Xin Univ Guelph Sch Engn Guelph ON N1G 2W1 Canada Natl Univ Def Technol Coll Mechatron & Automat Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9781467359252

Effective cooperation of multi-robots in unknown environments is essential in many robotic applications, such as environment exploration and target searching. In this paper, a combined hierarchical reinforcement learning approach, together with a designed cooperation strategy, is proposed for the real-time cooperation of multi-robots in completely unknown environments. Unlike other algorithms that need an explicit environment model or select parameters by trial and error, the proposed cooperation method obtains all the required parameters automatically through learning. By integrating segmental options with the traditional MAXQ algorithm, the cooperation hierarchy is built. In new tasks, the designed cooperation method can control the multi-robot system to complete the task effectively. the simulation results demonstrate that the proposed scheme is able to effectively and efficiently lead a team of robots to cooperatively accomplish target searching tasks in completely unknown environments.

关键词： Hierarchical reinforcement learning multi-robot cooperation complex unknown environment target searching

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Analyzing Collective Behavior in Evolutionary Swarm Robotic Systems Based on an Ethological Approach

Analyzing Collective Behavior in Evolutionary Swarm Robotic ...

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4th ieee International symposium on adaptive dynamic programming and reinforcement learning (adprl)

作者： Yasuda, Toshiyuki Wada, Nanami Ohkura, Kazuhiro Matsumura, Yoshiyuki Hiroshima Univ Grad Sch Engn 1-4-1 Kagamiyama Higashihiroshima 7398527 Japan Shinshu Univ Fac Text Sci & Technol Ueda Nagano 3868567 Japan

ISBN: (纸本)9781467359252

Swarm robotic systems are a type of multi-robot systems which generally consist of many homogeneous autonomous robots without any type of global controllers. Swarm robotics aims at designing desired collective behaviors through many interactions with other robots or their environment. Since a robotic swarm is controlled by an emergent way such as a result of self-organization by using robot learning or artificial evolution, no method has been known to grasp the macroscopic collective behavior in a practical sense, according to the best of our knowledge. In this paper, we propose a novel method for analyzing the collective behavior by introducing the concept of behavioral sequence, which stems from ethology. Analysis about behavioral sequence reveals the transition of robot's action from the viewpoint of specialization and helps us to understand the role of subgroups in a robotic swarm. Applying this method, we observe collective behavior in a foraging task of autonomous mobile robots.

关键词： Swarm Robotics Evolutionary Robotics Behavior Analysis Task Allocation Clustering Ethology Behavioral Sequence

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the Second Order Temporal Difference Error for Sarsa(λ)

The Second Order Temporal Difference Error for Sarsa(λ)

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4th ieee International symposium on adaptive dynamic programming and reinforcement learning (adprl)

作者： Fu, Qiming Liu, Quan Xiao, Fei Chen, Guixin Soochow Univ Dept Comp Sci & Technol Suzhou Peoples R China

ISBN: (纸本)9781467359252

Traditional reinforcement learning algorithms, such as Q-learning, Q(lambda), Sarsa, and Sarsa(lambda), update the action value function using temporal difference (TD) error, which is computed by the last action value function. From the perspective of the TD error, and with respect to the problems of low efficiency and slow convergence of the traditional Sarsa(lambda) algorithm, this paper defines the nth order TD Error, applies it in the traditional Sarsa(lambda) algorithm, and develops a fast Sarsa(lambda) algorithm based on the 2nd order TD Error. the algorithm adjusts the Q value with the second-order TD Error and broadcasts the TD Error into the whole state-action space, which speeds up the convergence of the algorithm. this paper also analyzes the convergence rate, and under the condition of one-step update, the results show that the number of iteration depends primarily on gamma, epsilon. Finally, using the proposed algorithm on the traditional reinforcement learning problems, the results show that the algorithm has both a faster convergence rate and better convergence performance.

关键词： reinforcement learning Markov Decision Process Second Order TD Error Eligibility Trace Sarsa(lambda) Algorithm

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Optimistic Planning for Continuous-Action Deterministic Systems

Optimistic Planning for Continuous-Action Deterministic Syst...

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4th ieee International symposium on adaptive dynamic programming and reinforcement learning (adprl)

作者： Busoniu, Lucian Daniels, Alexander Munos, Remi Babuska, Robert Univ Lorraine CRAN UMR 7039 Nancy France CNRS CRAN UMR 7039 Nancy France Delft Univ Technol DCSC Delft Netherlands INRIA Lille Nord Europe Team SequeL Lille France

ISBN: (纸本)9781467359252

We consider the class of online planning algorithms for optimal control, which compared to dynamic programming are relatively unaffected by large state dimensionality. We introduce a novel planning algorithm called SOOP that works for deterministic systems with continuous states and actions. SOOP is the first method to explore the true solution space, consisting of infinite sequences of continuous actions, without requiring knowledge about the smoothness of the system. SOOP can be used parameter-free at the cost of more model calls, but we also propose a more practical variant tuned by a parameter a, which balances finer discretization with longer planning horizons. Experiments on three problems show SOOP reliably ranks among the best algorithms, fully dominating competing methods when the problem requires both long horizons and fine discretization.

关键词： Solution space dynamic programming Smoothness deterministic Optimal control continuous action Discretization Horizon Planning

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