Results 1  10
of
213
Reinforcement learning: a survey
 Journal of Artificial Intelligence Research
, 1996
"... This paper surveys the field of reinforcement learning from a computerscience perspective. It is written to be accessible to researchers familiar with machine learning. Both the historical basis of the field and a broad selection of current work are summarized. Reinforcement learning is the problem ..."
Abstract

Cited by 1690 (26 self)
 Add to MetaCart
(Show Context)
This paper surveys the field of reinforcement learning from a computerscience perspective. It is written to be accessible to researchers familiar with machine learning. Both the historical basis of the field and a broad selection of current work are summarized. Reinforcement learning is the problem faced by an agent that learns behavior through trialanderror interactions with a dynamic environment. The work described here has a resemblance to work in psychology, but differs considerably in the details and in the use of the word "reinforcement." The paper discusses central issues of reinforcement learning, including trading off exploration and exploitation, establishing the foundations of the field via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state. It concludes with a survey of some implemented systems and an assessment of the practical utility of current methods for reinforcement learning.
Between MDPs and SemiMDPs: A Framework for Temporal Abstraction in Reinforcement Learning
 Artificial Intelligence
, 1999
"... Learning, planning, and representing knowledge at multiple levels of temporal abstraction are key, longstanding challenges for AI. In this paper we consider how these challenges can be addressed within the mathematical framework of reinforcement learning and Markov decision processes (MDPs). We ..."
Abstract

Cited by 560 (37 self)
 Add to MetaCart
(Show Context)
Learning, planning, and representing knowledge at multiple levels of temporal abstraction are key, longstanding challenges for AI. In this paper we consider how these challenges can be addressed within the mathematical framework of reinforcement learning and Markov decision processes (MDPs). We extend the usual notion of action in this framework to include optionsclosedloop policies for taking action over a period of time. Examples of options include picking up an object, going to lunch, and traveling to a distant city, as well as primitive actions such as muscle twitches and joint torques. Overall, we show that options enable temporally abstract knowledge and action to be included in the reinforcement learning framework in a natural and general way. In particular, we show that options may be used interchangeably with primitive actions in planning methods such as dynamic programming and in learning methods such as Qlearning.
LeastSquares Policy Iteration
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2003
"... We propose a new approach to reinforcement learning for control problems which combines valuefunction approximation with linear architectures and approximate policy iteration. This new approach ..."
Abstract

Cited by 461 (12 self)
 Add to MetaCart
(Show Context)
We propose a new approach to reinforcement learning for control problems which combines valuefunction approximation with linear architectures and approximate policy iteration. This new approach
Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition
 Journal of Artificial Intelligence Research
, 2000
"... This paper presents a new approach to hierarchical reinforcement learning based on decomposing the target Markov decision process (MDP) into a hierarchy of smaller MDPs and decomposing the value function of the target MDP into an additive combination of the value functions of the smaller MDPs. Th ..."
Abstract

Cited by 439 (6 self)
 Add to MetaCart
(Show Context)
This paper presents a new approach to hierarchical reinforcement learning based on decomposing the target Markov decision process (MDP) into a hierarchy of smaller MDPs and decomposing the value function of the target MDP into an additive combination of the value functions of the smaller MDPs. The decomposition, known as the MAXQ decomposition, has both a procedural semanticsas a subroutine hierarchyand a declarative semanticsas a representation of the value function of a hierarchical policy. MAXQ unifies and extends previous work on hierarchical reinforcement learning by Singh, Kaelbling, and Dayan and Hinton. It is based on the assumption that the programmer can identify useful subgoals and define subtasks that achieve these subgoals. By defining such subgoals, the programmer constrains the set of policies that need to be considered during reinforcement learning. The MAXQ value function decomposition can represent the value function of any policy that is consisten...
OnLine QLearning Using Connectionist Systems
, 1994
"... Reinforcement learning algorithms are a powerful machine learning technique. However, much of the work on these algorithms has been developed with regard to discrete finitestate Markovian problems, which is too restrictive for many realworld environments. Therefore, it is desirable to extend these ..."
Abstract

Cited by 383 (1 self)
 Add to MetaCart
Reinforcement learning algorithms are a powerful machine learning technique. However, much of the work on these algorithms has been developed with regard to discrete finitestate Markovian problems, which is too restrictive for many realworld environments. Therefore, it is desirable to extend these methods to high dimensional continuous statespaces, which requires the use of function approximation to generalise the information learnt by the system. In this report, the use of backpropagation neural networks (Rumelhart, Hinton and Williams 1986) is considered in this context. We consider a number of different algorithms based around QLearning (Watkins 1989) combined with the Temporal Difference algorithm (Sutton 1988), including a new algorithm (Modified Connectionist QLearning), and Q() (Peng and Williams 1994). In addition, we present algorithms for applying these updates online during trials, unlike backward replay used by Lin (1993) that requires waiting until the end of each t...
Classifier fitness based on accuracy
 Evolutionary Computation
, 1995
"... In many classifier systems, the classifier strength parameter serves as a predictor of future payoff and as the classifier’s fitness for the genetic algorithm. We investigate a classifier system, XCS, in which each classifier maintains a prediction of expected payoff, but the classifier’s fitness is ..."
Abstract

Cited by 341 (17 self)
 Add to MetaCart
In many classifier systems, the classifier strength parameter serves as a predictor of future payoff and as the classifier’s fitness for the genetic algorithm. We investigate a classifier system, XCS, in which each classifier maintains a prediction of expected payoff, but the classifier’s fitness is given by a measure of the prediction’s accuracy. The system executes the genetic algorithm in niches defined by the match sets, instead of panmictically. These aspects of XCS result in its population tending to form a complete and accurate mapping X x A + P from inputs and actions to payoff predictions. Further, XCS tends to evolve classifiers that are maximally general, subject to an accuracy criterion. Besides introducing a new direction for classifier system research, these properties of XCS make it suitable for a wide range of reinforcement learning situations where generalization over states is desirable.
Generalization in Reinforcement Learning: Safely Approximating the Value Function
 Advances in Neural Information Processing Systems 7
, 1995
"... To appear in: G. Tesauro, D. S. Touretzky and T. K. Leen, eds., Advances in Neural Information Processing Systems 7, MIT Press, Cambridge MA, 1995. A straightforward approach to the curse of dimensionality in reinforcement learning and dynamic programming is to replace the lookup table with a genera ..."
Abstract

Cited by 310 (3 self)
 Add to MetaCart
To appear in: G. Tesauro, D. S. Touretzky and T. K. Leen, eds., Advances in Neural Information Processing Systems 7, MIT Press, Cambridge MA, 1995. A straightforward approach to the curse of dimensionality in reinforcement learning and dynamic programming is to replace the lookup table with a generalizing function approximator such as a neural net. Although this has been successful in the domain of backgammon, there is no guarantee of convergence. In this paper, we show that the combination of dynamic programming and function approximation is not robust, and in even very benign cases, may produce an entirely wrong policy. We then introduce GrowSupport, a new algorithm which is safe from divergence yet can still reap the benefits of successful generalization. 1 INTRODUCTION Reinforcement learningthe problem of getting an agent to learn to act from sparse, delayed rewardshas been advanced by techniques based on dynamic programming (DP). These algorithms compute a value function ...
Reinforcement learning with hierarchies of machines
 Advances in Neural Information Processing Systems 10
, 1998
"... We present a new approach to reinforcement learning in which the policies considered by the learning process are constrained by hierarchies of partially specified machines. This allows for the use of prior knowledge to reduce the search space and provides a framework in which knowledge can be transf ..."
Abstract

Cited by 282 (11 self)
 Add to MetaCart
(Show Context)
We present a new approach to reinforcement learning in which the policies considered by the learning process are constrained by hierarchies of partially specified machines. This allows for the use of prior knowledge to reduce the search space and provides a framework in which knowledge can be transferred across problems and in which component solutions can be recombined to solve larger and more complicated problems. Our approach can be seen as providing a link between reinforcement learning and “behaviorbased ” or “teleoreactive ” approaches to control. We present provably convergent algorithms for problemsolving and learning with hierarchical machines and demonstrate their effectiveness on a problem with several thousand states. 1
Modeling Adaptive Autonomous Agents
 Artificial Life
, 1994
"... One category of researchers in artificial life is concerned with modeling and building socalled adaptive autonomous agents. Autonomous agents are systems that inhabit a dynamic, unpredictable environment in which they try to satisfy a set of timedependent goals or motivations. Agents are said to b ..."
Abstract

Cited by 251 (2 self)
 Add to MetaCart
(Show Context)
One category of researchers in artificial life is concerned with modeling and building socalled adaptive autonomous agents. Autonomous agents are systems that inhabit a dynamic, unpredictable environment in which they try to satisfy a set of timedependent goals or motivations. Agents are said to be adaptive if they improve their competence at dealing with these goals based on experience. Autonomous agents constitute a new approach to the study of artificial intelligence (AI) which is highly inspired by biology, in particular ethology, the study of animal behavior. Research in autonomous agents has brought about a new wave of excitement into the field of AI. This paper reflects on the state of the art of this new approach.