Application Of Hierarchical Reinforcement Learning In Engineering Domain

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Cooperative Multi-Agent Control Using Deep Reinforcement Learning

the controller, it requires extensive domain knowledge and hand engineering. Overall, deep reinforcement learning provides a more general way to solve multi-agent problems without the need for hand-crafted features and heuristics by allowing the neural network to learn those properties of the controller directly from raw obser-

Option and Constraint Generation using Work Domain Analysis

Study Goal: Use work domain analysis techniques from cognitive engineering to develop systematic method to generate options and constraints from experienced users How is our approach new: Provides a systematic way to mine a human s knowledge about a domain and to translate it to a hierarchical goal structure 8

arXiv:1904.10079v3 [cs.LG] 19 Jan 2021

ing standard reinforcement learning techniques, hierarchical methods, and basic imitation learning methods. 1.1.1 Domain Interest Minecraft is a compelling domain for the development of reinforcement and imitation learn-ing based methods because of the unique challenges it presents: Minecraft is a 3D, rst-3

A Synthesis of Automated Planning and Reinforcement Learning

Domain Approximation for Reinforcement LearnING (DARLING), a method that takes advantage of planning to constrain the behavior of the agent to rea-sonable choices, and of reinforcement learning to adapt to the environment, and increase the reliability of the decision making process. We demonstrate the ef-

Reinforcement Learning for Planning in High-Dimensional Domains

squares temporal difference learning approach, which learns the weights of features that are capable of modeling the problem correctly, can find optimal solutions to medium scale problems. Hierarchical reinforcement learning lets us define actions that operate over multiple time steps. Thereby, it provides

Learning to play Mario - Electrical Engineering and Computer

The rest of the paper is organized as follows. Section 2 reviews research work done on reinforcement learning in computer games. In sections 3, 4, 5 and 6, we give brief introduction to Reinforcement Learning, Hierarchical Reinforcement Learning, Object Oriented Representations in RL and Soar RL.

INTRODUCTION MACHINE LEARNING - Stanford AI Lab

achievement of learning in machines might help us understand how animals and humans learn. But there are important engineering reasons as well. Some of these are: Some tasks cannot be de ned well except by example; that is, we might be able to specify input/output pairs but not a concise relationship between inputs and desired outputs.

Hierarchical reinforcement learning via dynamic subspace

Hierarchical reinforcement learning via dynamic subspace search for multi-agent planning 3 Fig. 1.1 Work ow of the proposed hierarchical algorithm. A sub-environment is dynamically constructed as series of spatial-based state abstractions in the environment in a process called SubEnvSearch. Given this sub-environment, an agent performs

Economic Hierarchical Q-Learning - DASH Harvard

In Hierarchical Reinforcement Learning (HRL), a human programmer provides a task hierarchy in a domain, speci-fying how to decompose a task into subtasks. Given such a state decomposition, a reinforcement learning (RL) algo-rithm can exploit the structure of a problem domain to con-verge to a solution policy more quickly than RL with flat

Hierarchical Approaches - UNSW Engineering

inal problem. It is well known that the na ve application of reinforcement learning (RL) techniques fails to scale to more complex domains. This Chap-ter introduces hierarchical approaches to reinforcement learning that hold out the promise of reducing a reinforcement learning problems to a manage-able size.

A Collection of White Papers from the BDEC2 Workshop in San

A Community Machine Learning Commons for Advancing Machine Learning in Earth System Science Richard Loft 27 LUMI, the Pan-European Pre-Exascale Supercomputer Designed for the Converge of AI and HPC Pekka Manninen and Sebastian von Alfthan 29 Big Data Assimilation Incorporating Deep Learning with Phased Array Radar Data and

Statistical Spoken Dialogue Systems and the Challenges for

Hierarchical Deep Reinforcement Learning 20 T. Kulkarni et al (2016). Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation. arXiv:1604.06057. DQN θ DQN λ DQN λ DQN λ DQN θ b t b t+1 b t+N a t a t+1 g t g t a t+N g t g t+N Top meta-level Subgoal-level eg GetTime Next Subgoal

A Hierarchical Framework of Cloud Resource Allocation and

A Hierarchical Framework of Cloud Resource Allocation and Power Management Using Deep Reinforcement Learning Ning Liu , Zhe Li , Jielong Xu , Zhiyuan Xu, Sheng Lin, Qinru Qiu, Jian Tang, Yanzhi Wang Department of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY 13244, USA

Robustness and Generalization of Model-Free Learning for

Reinforcement learning is applied within each agent, to evolve a local state-to-action mapping in a continuous domain, thus leading to a system that exhibits developmental properties. This work addresses problem settings in the domain of kinematic control of dexterous-redundant robot manipulation systems.

Reinforcement Learning for Autonomous Vehicles

Reinforcement learning techniques have shown some promise in solving complex control problems. However, these methods sometimes fall short in environments requiring continual operation and with continuous state and action spaces, such as driving.

Tag-Aware Recommender System Based on Deep Reinforcement Learning

Jan 11, 2021 Deep Reinforcement Learning Zhiruo Zhao1, Lei Cao1, Xiliang Chen1, Zhixiong Xu1 1Command & Control Engineering College, Army Engineering University of PLA, Nanjing, CO 210000 China Abstract: Recently, the application of deep reinforcement learning in recommender system is

Convergent Reinforcement Learning for Hierarchical Reactive Plans

Many reinforcement learning algorithms represent behavior with Markov Decision Processes (MDPs). Although well understood, MDPs suffer from a problem of scale since the complexity of finding a solution grows so rapidly with the size of the domain. Research in hierarchical reinforcement learning addresses this concern

arXiv:1811.08275v1 [cs.AI] 17 Nov 2018

Hierarchical reinforcement learning (HRL) methods are known to reduce the computational complexity of RL ap-proaches by temporal and state abstraction in the form of decomposing the learning problem to a hierarchy of sev-eral sub-problems. Sub-goals refer to the local target states that not only provide easy access or high reinforcement

Scaling Ant Colony Optimization with Hierarchical

where αt is the learning rate and γ is the future reward discount factor[18]. Q-learning is proven to converge to the optimal action-value function Q∗ with probability of 1 [8]. 2.1.2 Hierarchical Reinforcement Learning HRL decomposes a complex reinforcement learning prob-lem into manageable parts. Techniques include separating

Knowledge Transfer for Deep Reinforcement Learning with

approach, termed hierarchical prioritized experience replay, to further accelerate the learning of the multi-task policy net-work. Numerous studies have shown that prioritizing the up-dates of reinforcement learning policy in an appropriate or-der could make the algorithm learn more efficiently (Moore and Atkeson 1993; Parr 1998).

Applying and Augmenting Deep Reinforcement Learning in

Fig. 2 Agent-environment interaction in reinforcement learning [8] 1.3 Deep Reinforcement Learning (DRL) DRL means the combination of RL with deep machine learn - ing methods. Deep machine learning is inspired by the research of structure and information processing of the neocortex. It tries to capture spatio-temporal dependencies and involves

Deep Learning and Reward Design for Reinforcement Learning

Deep Learning and Reward Design for Reinforcement Learning by Xiaoxiao Guo A dissertation submitted in partial ful llment of the requirements for the degree of Doctor of Philosophy (Computer Science and Engineering) in The University of Michigan 2017 Doctoral Committee: Professor Satinder Singh Baveja, Co-Chair Professor Richard L. Lewis, Co-Chair

Deep Reinforcement Learning: Framework, Applications, and

Index Terms Deep reinforcement learning, optimal control, cyber-physical systems, stochastic computing. I. INTRODUCTION Reinforcement learning provides us a mathematical frame-work for learning or deriving strategies or policies that map situations (i.e., states) into actions with the goal of maximizing an accumulative reward [1].

Designing of an E cient Classi er using Hierarchical

Q learning is a widely used reinforcement learning technique [5]-[12], suitable for online applications. The learning algorithm executes best possible action in a particular state to reach to the goal state, assigned by the agent( s). However, the at structure reinforcement learning (Q-Learning) su ers from

1 Unsupervised Machine Learning for Networking: Techniques

into five major categories: hierarchical learning, data cluster-ing, latent variable models, outlier detection, and reinforcement learning. Figure 2 depicts a taxonomy of unsupervised learning techniques and also notes the relevant sections in which these techniques are discussed. A. Hierarchical Learning

Efficient Sampling-Based Maximum Entropy Inverse

In terms of application domain, our work is closely related to [20] which also utilize inverse reinforcement learning to learn the reward functions from real driving trajectories. In the forward problem at each iteration, it directly solves the optimization problem and use the optimal trajectories to represent the expected feature counts.

Hierarchical reinforcement learning for situated natural

2.1 Reinforcement learning for NLG in interactive systems Reinforcement learning has become a popular method for optimising dialogue management decisions for flat (Singh et al. 2002) and hierarchical decision problems (Cuayahuitl´ et al. 2010). It has been appreciated especially for its ability of automatic

RePReL : Integrating Relational Planning and Reinforcement

Relational Planning and Reinforcement Learning We consider the problem of learning to act in relational do-mains with varying number of tasks and interacting objects. Before explaining the formulation of the RePReL frame-work, we motivate it with a simple toy example in Figure 1. Consider a taxi domain where the goal is to transport the

AIR FORCE INSTITUTE OF TECHNOLOGY

research goals. Chapter II provides a thorough overview of reinforcement learning, three hierarchical reinforcement learning techniques, focusing on one as the selected domain to test application against. Chapter II continues with the introduction and discussion of ant colony optimization and data clustering, again focusing on those

Multi-Resolution Airport Detection via Hierarchical

(a) Reinforcement learning saliency model (b) Hierarchical reinforcement learning saliency model Fig. 1. Flowchart of HRL saliency model. As we known, RSIs usually contain complicated back-ground such as sea, forest and land in one image. In these situations, it is difficult to guarantee airport target as sa lient

Active Hierarchical Imitation and Reinforcement Learning

learning algorithms in our hierarchical framework to enable the human to teach the agent high-level policy. B. Hierarchical Reinforcement Learning Building agents that can learn hierarchical policies is a long-standing problem in Reinforcement Learning, for example, decompose MDP into smaller MDPs [6] or feudal RL that

Special issue on adaptive and learning agents 2019

The third paper Domain adaptation-based transfer learning using adversarial networks by Shoeleh et al. (2020) proposes DATL AN, a technique that leverages adversarial domain adaptation principles to discover and transfer related skills between source and target reinforcement learning tasks. Experimental

Using Background Knowledge to Speed Reinforcement Learning in

Adaptation and learning, agent architectures, action selection and planning, hierarchical reinforcement learning. 1. INTRODUCTION Artificial agents are technological artifacts that perform tasks for people. They sense their world and relate perceptions to their tasks in order to identify, and then apply, an appropriate response.

Published as a conference paper at ICLR 2017

One of the main appealing aspects of hierarchical reinforcement learning (HRL) is to use skills to reduce the search complexity of the problem (Parr & Russell, 1998; Sutton et al., 1999; Dietterich, 2000). However, specifying a good hierarchy by hand requires domain-specific knowledge and

Scaling Ant Colony Optimization with Hierarchical

merges the methods developed for reinforcement learning and hierarchical reinforcement learning (HRL) with ACO to produce an algorithm that scales to solve large, complex optimization problems. This hierarchical ant colony opti-mization algorithm focuses on two problem domains, the taxi world and traveling salesman problems. The paper shows two

Proceedings of the Thirty-First AAAI Conference on Artificial

ment learning policies into a single multi-task policy via dis-tillation technique is known as policy distillation. When pol-icy distillation is under a deep reinforcement learning setting, due to the giant parameter size and the huge state space for each task domain, it requires extensive computational efforts to train the multi-task policy

Symbolic Planning and Model-Free Reinforcement Learning

Keywords: Reinforcement Learning Hierarchical Reinforcement Learning Goal Specification Automated Planning Symbolic Planning Acknowledgements We gratefully acknowledge funding from the Natural Sciences and Engineering Research Council of Canada (NSERC), Becas Chile (CONICYT), and Microsoft Research.

Learning Classifier Systems Approach for Automated Discovery

Learning Classifier Systems (LCSs) are rule based classifiers, often called Genetics Based Machine Learning tools, consisting of a set of rules and procedures for performing classifications and discovering rules using genetic and nongenetic operators. The most common applications of LCSs have been from the domain of reinforcement learning

Christopher Lee Simpkins

Adaptive Behavior Langauge), is a domain-specific language embedded Scala and includes abstrac-tions for direct expression of reinforcement learning problems. My programmer study suggested that integrating reinforcement learning into a programming language yields measurable software engfi-neering benefits.