Search Results - "IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning"

Approximate real-time optimal control based on sparse Gaussian process models by Boedecker, Joschka, Springenberg, Jost Tobias, Wulfing, Jan, Riedmiller, Martin

“…In this paper we present a fully automated approach to (approximate) optimal control of non-linear systems. Our algorithm jointly learns a non-parametric model…”
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Protecting against evaluation overfitting in empirical reinforcement learning by Whiteson, S., Tanner, B., Taylor, M. E., Stone, P.

“…Empirical evaluations play an important role in machine learning. However, the usefulness of any evaluation depends on the empirical methodology employed…”
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Reinforcement learning in the game of Othello: Learning against a fixed opponent and learning from self-play by van der Ree, Michiel, Wiering, Marco

“…This paper compares three strategies in using reinforcement learning algorithms to let an artificial agent learn to play the game of Othello. The three…”
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Model-based multi-objective reinforcement learning by Wiering, Marco A., Withagen, Maikel, Drugan, Madalina M.

“…This paper describes a novel multi-objective reinforcement learning algorithm. The proposed algorithm first learns a model of the multi-objective sequential…”
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Pseudo-MDPs and factored linear action models by Hengshuai Yao, Szepesvari, Csaba, Pires, Bernardo Avila, Xinhua Zhang

“…In this paper we introduce the concept of pseudo-MDPs to develop abstractions. Pseudo-MDPs relax the requirement that the transition kernel has to be a…”
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Reinforcement learning algorithms for solving classification problems by Wiering, M. A., van Hasselt, H., Pietersma, Auke-Dirk, Schomaker, L.

“…We describe a new framework for applying reinforcement learning (RL) algorithms to solve classification tasks by letting an agent act on the inputs and learn…”
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Approximate reinforcement learning: An overview by Busoniu, L., Ernst, D., De Schutter, B., Babuska, R.

“…Reinforcement learning (RL) allows agents to learn how to optimally interact with complex environments. Fueled by recent advances in approximation-based…”
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A comparison of approximate dynamic programming techniques on benchmark energy storage problems: Does anything work? by Jiang, Daniel R., Pham, Thuy V., Powell, Warren B., Salas, Daniel F., Scott, Warren R.

“…As more renewable, yet volatile, forms of energy like solar and wind are being incorporated into the grid, the problem of finding optimal control policies for…”
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Exploring the relationship of reward and punishment in reinforcement learning by Lowe, Robert, Ziemke, Tom

“…We present a reinforcement learning algorithm based on Dyna-Sarsa that utilizes separate representations of reward and punishment when guiding state-action…”
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Real-time tracking on adaptive critic design with uniformly ultimately bounded condition by Zhen Ni, Xiao Fang, Haibo He, Dongbin Zhao, Xin Xu

“…In this paper, we proposed a new nonlinear tracking controller based on heuristic dynamic programming (HDP) with the tracking filter. Specifically, we…”
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Multi-objective reinforcement learning for AUV thruster failure recovery by Ahmadzadeh, Seyed Reza, Kormushev, Petar, Caldwell, Darwin G.

“…This paper investigates learning approaches for discovering fault-tolerant control policies to overcome thruster failures in Autonomous Underwater Vehicles…”
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Parametric value function approximation: A unified view by Geist, M., Pietquin, O.

“…Reinforcement learning (RL) is a machine learning answer to the optimal control problem. It consists of learning an optimal control policy through interactions…”
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Data-driven partially observable dynamic processes using adaptive dynamic programming by Xiangnan Zhong, Zhen Ni, Yufei Tang, Haibo He

“…Adaptive dynamic programming (ADP) has been widely recognized as one of the "core methodologies" to achieve optimal control for intelligent systems in Markov…”
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Annealing-pareto multi-objective multi-armed bandit algorithm by Yahyaa, Saba Q., Drugan, Madalina M., Manderick, Bernard

“…In the stochastic multi-objective multi-armed bandit (or MOMAB), arms generate a vector of stochastic rewards, one per objective, instead of a single scalar…”
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Grounding subgoals in information transitions by van Dijk, S. G., Polani, D.

“…In reinforcement learning problems, the construction of subgoals has been identified as an important step to speed up learning and to enable skill transfer…”
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Information-theoretic stochastic optimal control via incremental sampling-based algorithms by Arslan, Oktay, Theodorou, Evangelos A., Tsiotras, Panagiotis

“…This paper considers optimal control of dynamical systems which are represented by nonlinear stochastic differential equations. It is well-known that the…”
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Path integral control and bounded rationality by Braun, D. A., Ortega, P. A., Theodorou, E., Schaal, S.

“…Path integral methods have recently been shown to be applicable to a very general class of optimal control problems. Here we examine the path integral…”
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Feedback controller parameterizations for Reinforcement Learning by Roberts, John W., Manchester, Ian R., Tedrake, Russ

“…Reinforcement Learning offers a very general framework for learning controllers, but its effectiveness is closely tied to the controller parameterization used…”
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Using approximate dynamic programming for estimating the revenues of a hydrogen-based high-capacity storage device by Francois-Lavet, Vincent, Fonteneau, Raphael, Ernst, Damien

“…This paper proposes a methodology to estimate the maximum revenue that can be generated by a company that operates a high-capacity storage device to buy or…”
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Pareto Upper Confidence Bounds algorithms: An empirical study by Drugan, Madalina M., Nowe, Ann, Manderick, Bernard

“…Many real-world stochastic environments are inherently multi-objective environments with conflicting objectives. The multi-objective multi-armed bandits…”
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