Results 1  10
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50
Policy Gradient Methods for Reinforcement Learning with Function Approximation
, 1999
"... Function approximation is essential to reinforcement learning, but the standard approach of approximating a value function and determining a policy from it has so far proven theoretically intractable. In this paper we explore an alternative approach in which the policy is explicitly represented by i ..."
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Cited by 433 (20 self)
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Function approximation is essential to reinforcement learning, but the standard approach of approximating a value function and determining a policy from it has so far proven theoretically intractable. In this paper we explore an alternative approach in which the policy is explicitly represented by its own function approximator, independent of the value function, and is updated according to the gradient of expected reward with respect to the policy parameters. Williams’s REINFORCE method and actor–critic methods are examples of this approach. Our main new result is to show that the gradient can be written in a form suitable for estimation from experience aided by an approximate actionvalue or advantage function. Using this result, we prove for the first time that a version of policy iteration with arbitrary differentiable function approximation is convergent to a locally optimal policy.
ActorCritic Algorithms
 SIAM JOURNAL ON CONTROL AND OPTIMIZATION
, 2001
"... In this paper, we propose and analyze a class of actorcritic algorithms. These are twotimescale algorithms in which the critic uses temporal difference (TD) learning with a linearly parameterized approximation architecture, and the actor is updated in an approximate gradient direction based on in ..."
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Cited by 242 (1 self)
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In this paper, we propose and analyze a class of actorcritic algorithms. These are twotimescale algorithms in which the critic uses temporal difference (TD) learning with a linearly parameterized approximation architecture, and the actor is updated in an approximate gradient direction based on information provided by the critic. We show that the features for the critic should ideally span a subspace prescribed by the choice of parameterization of the actor. We study actorcritic algorithms for Markov decision processes with general state and action spaces. We state and prove two results regarding their convergence.
SimulationBased Optimization of Markov Reward Processes
 IEEE Transactions on Automatic Control
, 1998
"... We propose a simulationbased algorithm for optimizing the average reward in a Markov Reward Process that depends on a set of parameters. As a special case, the method applies to Markov Decision Processes where optimization takes place within a parametrized set of policies. The algorithm involves th ..."
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Cited by 103 (1 self)
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We propose a simulationbased algorithm for optimizing the average reward in a Markov Reward Process that depends on a set of parameters. As a special case, the method applies to Markov Decision Processes where optimization takes place within a parametrized set of policies. The algorithm involves the simulation of a single sample path, and can be implemented online. Aconvergence result (with probability1)isprovided.
Direct gradientbased reinforcement learning: I. gradient estimation algorithms
 National University
, 1999
"... In [2] we introduced ¢¡¤£¦¥¨§¦¡, an algorithm for computing arbitrarily accurate approximations to the performance gradient of parameterized partially observable Markov decision processes ( ¡©£¦¥¨§¦ ¡ s). The algorithm’s chief advantages are that it requires only a single sample path of the underly ..."
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Cited by 83 (3 self)
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In [2] we introduced ¢¡¤£¦¥¨§¦¡, an algorithm for computing arbitrarily accurate approximations to the performance gradient of parameterized partially observable Markov decision processes ( ¡©£¦¥¨§¦ ¡ s). The algorithm’s chief advantages are that it requires only a single sample path of the underlying Markov chain, it uses only one ���� � ������ � free parameter which has a natural interpretation in terms of biasvariance tradeoff, and it requires no knowledge of the underlying state. In addition, the algorithm can be applied to infinite state, control and observation spaces.
Experiments with InfiniteHorizon, PolicyGradient Estimation
 Journal of Artificial Intelligence Research
, 2001
"... In this paper, we present algorithms that perform gradient ascent of the average reward in a partially observable Markov decision process (POMDP). These algorithms are based on GPOMDP, an algorithm introduced in a companion paper (Baxter & Bartlett, 2001), which computes biased estimates of t ..."
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Cited by 80 (3 self)
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In this paper, we present algorithms that perform gradient ascent of the average reward in a partially observable Markov decision process (POMDP). These algorithms are based on GPOMDP, an algorithm introduced in a companion paper (Baxter & Bartlett, 2001), which computes biased estimates of the performance gradient in POMDPs. The algorithm's chief advantages are that it uses only one free parameter 2 [0; 1), which has a natural interpretation in terms of biasvariance tradeoff, it requires no knowledge of the underlying state, and it can be applied to infinite state, control and observation spaces. We show how the gradient estimates produced by GPOMDP can be used to perform gradient ascent, both with a traditional stochasticgradient algorithm, and with an algorithm based on conjugategradients that utilizes gradient information to bracket maxima in line searches. Experimental results are presented illustrating both the theoretical results of Baxter and Bartlett (2001) on a toy problem, and practical aspects of the algorithms on a number of more realistic problems. 1.
Reinforcement Learning in POMDP's via Direct Gradient Ascent
 In Proc. 17th International Conf. on Machine Learning
, 2000
"... This paper discusses theoretical and experimental aspects of gradientbased approaches to the direct optimization of policy performance in controlled POMDPs. We introduce GPOMDP, a REINFORCElike algorithm for estimating an approximation to the gradient of the average reward as a function of ..."
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Cited by 78 (2 self)
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This paper discusses theoretical and experimental aspects of gradientbased approaches to the direct optimization of policy performance in controlled POMDPs. We introduce GPOMDP, a REINFORCElike algorithm for estimating an approximation to the gradient of the average reward as a function of the parameters of a stochastic policy. The algorithm's chief advantages are that it requires only a single sample path of the underlying Markov chain, it uses only one free parameter 2 [0; 1), which has a natural interpretation in terms of biasvariance tradeoff, and it requires no knowledge of the underlying state. We prove convergence of GPOMDP and show how the gradient estimates produced by GPOMDP can be used in a conjugategradient procedure to find local optima of the average reward. 1. Introduction "Reinforcement learning" is used to describe the general problem of training an agent to choose its actions so as to increase its longterm average reward. The structure of th...
Incremental Natural ActorCritic Algorithms
"... We present four new reinforcement learning algorithms based on actorcritic and naturalgradient ideas, and provide their convergence proofs. Actorcritic reinforcement learning methods are online approximations to policy iteration in which the valuefunction parameters are estimated using temporal ..."
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Cited by 71 (8 self)
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We present four new reinforcement learning algorithms based on actorcritic and naturalgradient ideas, and provide their convergence proofs. Actorcritic reinforcement learning methods are online approximations to policy iteration in which the valuefunction parameters are estimated using temporal difference learning and the policy parameters are updated by stochastic gradient descent. Methods based on policy gradients in this way are of special interest because of their compatibility with function approximation methods, which are needed to handle large or infinite state spaces. The use of temporal difference learning in this way is of interest because in many applications it dramatically reduces the variance of the gradient estimates. The use of the natural gradient is of interest because it can produce better conditioned parameterizations and has been shown to further reduce variance in some cases. Our results extend prior twotimescale convergence results for actorcritic methods by Konda and Tsitsiklis by using temporal difference learning in the actor and by incorporating natural gradients, and they extend prior empirical studies of natural actorcritic methods by Peters, Vijayakumar and Schaal by providing the first convergence proofs and the first fully incremental algorithms. 1
SimulationBased Methods for Markov Decision Processes
 Labortory for Information and Decision Systems, MIT
, 1998
"... Markov decision processes have been a popular paradigm for sequential decision making under uncertainty. Dynamic programming provides a framework for studying such problems, as well as for devising algorithms to compute an optimal control policy. Dynamic programming methods rely on a suitably define ..."
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Cited by 28 (0 self)
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Markov decision processes have been a popular paradigm for sequential decision making under uncertainty. Dynamic programming provides a framework for studying such problems, as well as for devising algorithms to compute an optimal control policy. Dynamic programming methods rely on a suitably defined value function that has to be computed for every state in the state space. However, many interesting problems involve very large state spaces ("curse of dimensionality"), which prohibits the application of dynamic programming. In addition, dynamic programming assumes the availability of an exact model, in the form of transition probabilities ("curse of modeling"). In many practical situations, such a model is not available and one must resort to simulation or experimentation with an actual system. For all of these reasons, dynamic programming in its pure form may be inapplicable. In this thesis we study an approach for overcoming these difficulties where we use (a) compact (parametric) re...
Adaptive importance sampling technique for markov chains using stochastic approximation
 Operations Research
, 2004
"... Abstract For a discretetime finitestate Markov chain, we develop an adaptive importance sampling scheme to estimate the expected total cost before hitting a set of terminal states. This scheme updates the change of measure at every transition using constant or decreasing stepsize stochastic appro ..."
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Cited by 18 (7 self)
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Abstract For a discretetime finitestate Markov chain, we develop an adaptive importance sampling scheme to estimate the expected total cost before hitting a set of terminal states. This scheme updates the change of measure at every transition using constant or decreasing stepsize stochastic approximation. The updates are shown to concentrate asymptotically in a neighborhood of the desired zero variance estimator. Through simulation experiments on simple Markovian queues, we observe that the proposed technique performs very well in estimating performance measures related to rare events associated with queue lengths exceeding prescribed thresholds. We include performance comparisons of the proposed algorithm with existing adaptive importance sampling algorithms on a small example. We also discuss the extension of the technique to estimate the infinite horizon expected discounted cost and the expected average cost.
KernelBased Reinforcement Learning in AverageCost Problems: An Application to Optimal Portfolio Choice
 Advances in Neural Information Processing Systems
, 2000
"... Many approaches to reinforcement learning combine neural networks or other parametric function approximators with a form of temporaldifference learning to estimate the value function of a Markov Decision Process. A significant disadvantage of those procedures is that the resulting learning algo ..."
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Cited by 17 (3 self)
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Many approaches to reinforcement learning combine neural networks or other parametric function approximators with a form of temporaldifference learning to estimate the value function of a Markov Decision Process. A significant disadvantage of those procedures is that the resulting learning algorithms are frequently unstable.