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High-order local dynamic programming

High-order local dynamic programming

引用

ieee symposium on adaptive dynamic programming and reinforcement learning, (ADPRL)

作者： Yuval Tassa Emanuel Todorov Interdisciplinary Center of Neural Computation Hebrew University Jerusalem Israel Applied Mathematics and Computer Science & Engineering University of Washington Seattle USA

We describe a new local dynamic programming algorithm for solving stochastic continuous Optimal Control problems. We use cubature integration to both propagate the state distribution and perform the Bellman backup. The algorithm can approximate the local policy and cost-to-go with arbitrary function bases. We compare the classic quadratic cost-to-go/linear-feedback controller to a cubic cost-to-go/quadratic policy controller on a 10-dimensional simulated swimming robot, and find that the higher order approximation yields a more general policy with a larger basin of attraction.

关键词： Approximation methods Heuristic algorithms Mathematical model Equations Trajectory dynamic programming Noise

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Model-free H_∞ Stochastic Optimal Design for Unknown Linear Networked Control System Zero-sum Games via Q-learning

Model-free <i>H</i><sub>∞</sub> Stochastic Optimal Design f...

引用

ieee International symposium on Intelligent Control (ISIC)/ieee Multi-Conference on Systems and Control (MSC)

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

ISBN: (纸本)9781457711039

In this paper, stochastic optimal strategy for unknown linear networked control system (NCS) quadratic zero-sum games related to H-infinity optimal control in the presence of random delays and packet losses is solved in forward-in-time manner. This approach does not require the knowledge of the system matrices since it uses Q-learning. The proposed stochastic optimal control approach, referred as adaptive dynamic programming (ADP), involves solving the action dependent Q-function Q(z,u,d) of the zero-sum game instead of solving the state dependent value function J(z) which satisfies a corresponding Game Theoretic Riccati equation (GRE). An adaptive estimator (AE) is proposed to learn the Q-function online and value and policy iterations are not needed unlike in traditional ADP schemes. Update laws for tuning the unknown parameters of adaptive estimator (AE) are derived. Lyapunov theory is used to show that all signals are asymptotic stable (AS) and that the approximated control and disturbance signals converge to optimal control and disturbance inputs. Simulation results are included to show the effectiveness of the scheme.

关键词： Networked control systems

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A reinforcement learning approach for sequential mastery testing

A reinforcement learning approach for sequential mastery tes...

引用

ieee symposium on adaptive dynamic programming and reinforcement learning, (ADPRL)

作者： El-Sayed M. El-Alfy College of Computer Sciences and Engineering King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia

This paper explores a novel application for reinforcement learning (RL) techniques to sequential mastery testing. In such systems, the goal is to classify each examined person, using the minimal number of test items, as master or non-master. Using RL, an intelligent agent autonomously learns from interactions to administer more informative and effective variable-length tests. Empirical results are also provided to evaluate the performance of the proposed approach as compared to two common approaches for variable-length testing (Bayesian decision and sequential probability ratio test) as well as to the fixed-length testing.

关键词： Testing Bayesian methods learning Accuracy Decision theory Measurement Adaptation models

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Higher-level application of adaptive dynamic programming/reinforcement learning - a next phase for controls and system identification?

Higher-level application of Adaptive Dynamic Programming/Rei...

引用

ieee symposium on adaptive dynamic programming and reinforcement learning, (ADPRL)

作者： George G. Lendaris Systems Science Graduate Program Portland State University Portland OR USA

In previous work it was shown that adaptive-Critic-type Approximate dynamic programming could be applied in a “higher-level” way to create autonomous agents capable of using experience to discern context and select optimal, context-dependent control policies. Early experiments with this approach were based on full a priori knowledge of the system being monitored. The experiments reported in this paper, using small neural networks representing families of mappings, were designed to explore what happens when knowledge of the system is less precise. Results of these experiments show that agents trained with this approach perform well when subject to even large amounts of noise or when employing (slightly) imperfect models. The results also suggest that aspects of this method of context discernment are consistent with our intuition about human learning. The insights gained from these explorations can be used to guide further efforts for developing this approach into a general methodology for solving arbitrary identification and control problems.

关键词： Context Artificial neural networks Context modeling Process control System identification Training Humans

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An approximate dynamic programming based controller for an underactuated 6DoF quadrotor

An approximate Dynamic Programming based controller for an u...

引用

ieee symposium on adaptive dynamic programming and reinforcement learning, (ADPRL)

作者： Emanuel Stingu Frank L. Lewis Automation & Robotics Research Institute University of Texas Arlington Arlington TX USA

This paper discusses how the principles of adaptive dynamic programming (ADP) can be applied to the control of a quadrotor helicopter platform flying in an uncontrolled environment and subjected to various disturbances and model uncertainties. ADP is based on reinforcement learning using an actor-critic structure. Due to the complexity of the quadrotor system, the learning process has to use as much information as possible about the system and the environment. Various methods to improve the learning speed and efficiency are presented. Neural networks with local activation functions are used as function approximators because the state-space can not be explored efficiently due to its size and the limited time available. The complex dynamics is controlled by a single critic and by multiple actors thus avoiding the curse of dimensionality. After a number of iterations, the overall actor-critic structure stores information (knowledge) about the system dynamics and the optimal controller that can accomplish the explicit or implicit goal specified in the cost function.

关键词： Equations Optimal control Rotors Heuristic algorithms Neurons Artificial neural networks Approximation methods

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Grounding subgoals in information transitions

Grounding subgoals in information transitions

引用

ieee symposium on adaptive dynamic programming and reinforcement learning, (ADPRL)

作者： Sander G. van Dijk Daniel Polani Adaptive Systems Research Group University of Herfordshire Hatfield UK

In reinforcement learning problems, the construction of subgoals has been identified as an important step to speed up learning and to enable skill transfer. For this purpose, one typically extracts states from various saliency properties of an MDP transition graph, most notably bottleneck states. Here we introduce an alternative approach to this problem: assuming a family of MDPs with multiple goals but with a fixed transition graph, we introduce the relevant goal information as the amount of Shannon information that the agent needs to maintain about the current goal at a given state to select the appropriate action. We show that there are distinct transition states in the MDP at which new relevant goal information has to be considered for selecting the next action. We argue that these transition states can be interpreted as subgoals for the current task class, and we use these states to automatically create a hierarchical policy, according to the well-established Options model for hierarchical reinforcement learning.

关键词： learning Entropy Mutual information Uncertainty History Probability distribution

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adaptive sample collection using active learning for kernel-based approximate policy iteration

Adaptive sample collection using active learning for kernel-...

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ieee symposium on adaptive dynamic programming and reinforcement learning, (ADPRL)

作者： Chunming Liu Xin Xu Haiyun Hu Bin Dai College of Mechatronics and Automation National University of Defense Technology Changsha China

Approximate policy iteration (API) has been shown to be a class of reinforcement learning methods with stability and sample efficiency. However, sample collection is still an open problem which is critical to the performance of API methods. In this paper, a novel adaptive sample collection strategy using active learning-based exploration is proposed to enhance the performance of kernel-based API. In this strategy, an online kernel-based least squares policy iteration (KLSPI) method is adopted to construct nonlinear features and approximate the Q-function simultaneously. Therefore, more representative samples can be obtained for value function approximation. Simulation results on typical learning control problems illustrate that by using the proposed strategy, the performance of KLSPI can be improved remarkably.

关键词： Kernel Algorithm design and analysis Function approximation Approximation algorithms learning Dictionaries

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N-step optimal time-invariant trajectory tracking control for a class of nonlinear systems

N-step optimal time-invariant trajectory tracking control fo...

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ieee symposium on adaptive dynamic programming and reinforcement learning, (ADPRL)

作者： Ruizhuo Song Huaguang Zhang School of Information Science and Engineering Northeastern University Shenyang China

In this paper, the time-invariant trajectory tracking control problem under N-step control is solved by finite horizon approximate dynamic programming (ADP) algorithms. At first, we convert the tracking control problem for time-invariant trajectory into a output regulation problem. The cost function guarantees the energy is minimum. Secondly, the regulation control scheme is proposed using finite horizon ADP technique to obtain the N-step control. Then two theorems are used to prove the convergence of the proposed control algorithm. Finally, the simulation is given to demonstrate the effectiveness and feasibility of the control scheme.

关键词： Trajectory Cost function Nonlinear systems Optimal control dynamic programming Convergence Mathematical model

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Protecting against evaluation overfitting in empirical reinforcement learning

Protecting against evaluation overfitting in empirical reinf...

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ieee symposium on adaptive dynamic programming and reinforcement learning, (ADPRL)

作者： Shimon Whiteson Brian Tanner Matthew E. Taylor Peter Stone Informatics Institute University of Amsterdam Netherlands Department of Computing Science University of Alberta Canada Department of Computer Science Lafayette College USA Department of Computer Science University of Texas Austin USA

Empirical evaluations play an important role in machine learning. However, the usefulness of any evaluation depends on the empirical methodology employed. Designing good empirical methodologies is difficult in part because agents can overfit test evaluations and thereby obtain misleadingly high scores. We argue that reinforcement learning is particularly vulnerable to environment overfitting and propose as a remedy generalized methodologies, in which evaluations are based on multiple environments sampled from a distribution. In addition, we consider how to summarize performance when scores from different environments may not have commensurate values. Finally, we present proof-of-concept results demonstrating how these methodologies can validate an intuitively useful range-adaptive tile coding method.

关键词： Uncertainty Algorithm design and analysis Tiles learning Tuning Machine learning Supervised learning

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Approximate reinforcement learning: An overview

Approximate reinforcement learning: An overview

引用

ieee symposium on adaptive dynamic programming and reinforcement learning, (ADPRL)

作者： Lucian Buşoniu Damien Ernst Bart De Schutter Robert Babuška Delft Center of Systems & Control Delft University of Technnology Netherlands FRS-FNRS Systems and Modeling Unit University of Liège Belgium

reinforcement learning (RL) allows agents to learn how to optimally interact with complex environments. Fueled by recent advances in approximation-based algorithms, RL has obtained impressive successes in robotics, artificial intelligence, control, operations research, etc. However, the scarcity of survey papers about approximate RL makes it difficult for newcomers to grasp this intricate field. With the present overview, we take a step toward alleviating this situation. We review methods for approximate RL, starting from their dynamic programming roots and organizing them into three major classes: approximate value iteration, policy iteration, and policy search. Each class is subdivided into representative categories, highlighting among others offline and online algorithms, policy gradient methods, and simulation-based techniques. We also compare the different categories of methods, and outline possible ways to enhance the reviewed algorithms.

关键词： Approximation algorithms Equations Function approximation Trajectory Markov processes Mathematical model

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