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126
Perseus: Randomized pointbased value iteration for POMDPs
 Journal of Artificial Intelligence Research
, 2005
"... Partially observable Markov decision processes (POMDPs) form an attractive and principled framework for agent planning under uncertainty. Pointbased approximate techniques for POMDPs compute a policy based on a finite set of points collected in advance from the agent’s belief space. We present a ra ..."
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Cited by 141 (11 self)
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Partially observable Markov decision processes (POMDPs) form an attractive and principled framework for agent planning under uncertainty. Pointbased approximate techniques for POMDPs compute a policy based on a finite set of points collected in advance from the agent’s belief space. We present a randomized pointbased value iteration algorithm called Perseus. The algorithm performs approximate value backup stages, ensuring that in each backup stage the value of each point in the belief set is improved; the key observation is that a single backup may improve the value of many belief points. Contrary to other pointbased methods, Perseus backs up only a (randomly selected) subset of points in the belief set, sufficient for improving the value of each belief point in the set. We show how the same idea can be extended to dealing with continuous action spaces. Experimental results show the potential of Perseus in large scale POMDP problems. 1.
Policy Gradient Reinforcement Learning for Fast Quadrupedal Locomotion
 in Proceedings of the IEEE International Conference on Robotics and Automation
, 2004
"... This paper presents a machine learning approach to optimizing a quadrupedal trot gait for forward speed. Given a parameterized walk designed for a specific robot, we propose using a form of policy gradient reinforcement learning to automatically search the set of possible parameters with the goal of ..."
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Cited by 103 (14 self)
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This paper presents a machine learning approach to optimizing a quadrupedal trot gait for forward speed. Given a parameterized walk designed for a specific robot, we propose using a form of policy gradient reinforcement learning to automatically search the set of possible parameters with the goal of finding the fastest possible walk. We implement and test our approach on a commercially available quadrupedal robot platform, namely the Sony Aibo robot. After about three hours of learning, all on the physical robots and with no human intervention other than to change the batteries, the robots achieved a gait faster than any previously known gait for the Aibo, significantly outperforming a variety of existing handcoded and learned solutions.
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 the p ..."
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Cited by 59 (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.
Stochastic policy gradient reinforcement learning on a simple 3D biped
 Proc. of the 10th Int. Conf. on Intelligent Robots and Systems
, 2004
"... Abstract — We present a learning system which is able to quickly and reliably acquire a robust feedback control policy for 3D dynamic walking from a blankslate using only trials implemented on our physical robot. The robot begins walking within a minute and learning converges in approximately 20 mi ..."
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Cited by 52 (5 self)
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Abstract — We present a learning system which is able to quickly and reliably acquire a robust feedback control policy for 3D dynamic walking from a blankslate using only trials implemented on our physical robot. The robot begins walking within a minute and learning converges in approximately 20 minutes. This success can be attributed to the mechanics of our robot, which are modeled after a passive dynamic walker, and to a dramatic reduction in the dimensionality of the learning problem. We reduce the dimensionality by designing a robot with only 6 internal degrees of freedom and 4 actuators, by decomposing the control system in the frontal and sagittal planes, and by formulating the learning problem on the discrete return map dynamics. We apply a stochastic policy gradient algorithm to this reduced problem and decrease the variance of the update using a statebased estimate of the expected cost. This optimized learning system works quickly enough that the robot is able to continually adapt to the terrain as it walks. I.
Transfer Learning for Reinforcement Learning Domains: A Survey
"... The reinforcement learning paradigm is a popular way to address problems that have only limited environmental feedback, rather than correctly labeled examples, as is common in other machine learning contexts. While significant progress has been made to improve learning in a single task, the idea of ..."
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Cited by 48 (7 self)
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The reinforcement learning paradigm is a popular way to address problems that have only limited environmental feedback, rather than correctly labeled examples, as is common in other machine learning contexts. While significant progress has been made to improve learning in a single task, the idea of transfer learning has only recently been applied to reinforcement learning tasks. The core idea of transfer is that experience gained in learning to perform one task can help improve learning performance in a related, but different, task. In this article we present a framework that classifies transfer learning methods in terms of their capabilities and goals, and then use it to survey the existing literature, as well as to suggest future directions for transfer learning work.
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 41 (3 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
Dynamic Abstraction in Reinforcement Learning via Clustering
 In Proceedings of the TwentyFirst International Conference on Machine Learning
, 2004
"... We consider a graph theoretic approach for automatic construction of options in a dynamic environment. A map of the environment is generated online by the learning agent, representing the topological structure of the state transitions. A clustering algorithm is then used to partition the stat ..."
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Cited by 40 (0 self)
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We consider a graph theoretic approach for automatic construction of options in a dynamic environment. A map of the environment is generated online by the learning agent, representing the topological structure of the state transitions. A clustering algorithm is then used to partition the state space to di#erent regions. Policies for reaching the di#erent parts of the space are separately learned and added to the model in a form of options (macroactions). The options are used for accelerating the QLearning algorithm. We extend the basic algorithm and consider building a map that includes preliminary indication of the location of "interesting " regions of the state space, where the value gradient is significant and additional exploration might be beneficial. Experiments indicate significant speedups, especially in the initial learning phase.
Variance Reduction Techniques for Gradient Estimates in Reinforcement Learning
 Journal of Machine Learning Research
, 2001
"... We consider the use of two additive control variate methods to reduce the variance of performance gradient estimates in reinforcement learning problems. The first approach we consider is the baseline method, in which a function of the current state is added to the discounted value estimate. We relat ..."
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Cited by 40 (1 self)
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We consider the use of two additive control variate methods to reduce the variance of performance gradient estimates in reinforcement learning problems. The first approach we consider is the baseline method, in which a function of the current state is added to the discounted value estimate. We relate the performance of these methods, which use sample paths, to the variance of estimates based on iid data. We derive the baseline function that minimizes this variance, and we show that the variance for any baseline is the sum of the optimal variance and a weighted squared distance to the optimal baseline. We show that the widely used average discounted value baseline (where the reward is replaced by the difference between the reward and its expectation) is suboptimal. The second approach we consider is the actorcritic method, which uses an approximate value function. We give bounds on the expected squared error of its estimates. We show that minimizing distance to the true value function is suboptimal in general; we provide an example for which the true value function gives an estimate with positive variance, but the optimal value function gives an unbiased estimate with zero variance. Our bounds suggest algorithms to estimate the gradient of the performance of parameterized baseline or value functions. We present preliminary experiments that illustrate the performance improvements on a simple control problem.
PointBased Value Iteration for Continuous POMDPs
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2006
"... We propose a novel approach to optimize Partially Observable Markov Decisions Processes (POMDPs) defined on continuous spaces. To date, most algorithms for modelbased POMDPs are restricted to discrete states, actions, and observations, but many realworld problems such as, for instance, robot na ..."
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Cited by 34 (2 self)
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We propose a novel approach to optimize Partially Observable Markov Decisions Processes (POMDPs) defined on continuous spaces. To date, most algorithms for modelbased POMDPs are restricted to discrete states, actions, and observations, but many realworld problems such as, for instance, robot navigation, are naturally defined on continuous spaces. In this work, we demonstrate that the value function for continuous POMDPs is convex in the beliefs over continuous state spaces, and piecewiselinear convex for the particular case of discrete observations and actions but still continuous states. We also demonstrate that continuous Bellman backups are contracting and isotonic ensuring the monotonic convergence of valueiteration algorithms. Relying on those properties, we extend the PERSEUS algorithm, originally developed for discrete POMDPs, to work in continuous state spaces by representing the observation, transition, and reward models using Gaussian mixtures, and the beliefs using Gaussian mixtures or particle sets. With these representations, the integrals that appear in the Bellman backup can be computed in closed form and, therefore, the algorithm is computationally feasible. Finally, we further extend PERSEUS to deal with continuous action and observation sets by designing effective sampling approaches.