Model-based Reinforcement Learning: A Survey (Foundations and Trends® in Machine Learning) - Image 1

Out of Stock

This item is currently unavailable

Model-based Reinforcement Learning: A Survey (Foundations and Trends® in Machine Learning)

Architecture & Microprocessors

Out of Stock

Price data last checked 21 day(s) ago - will refresh soon

View at Amazon

Price History & Forecast

Last 70 days • 70 data points (No recent data available)

Historical

Generating forecast...

Price Distribution

Price distribution over 70 days • 2 price levels

Days at Price

Current Price

Price Analysis

Most common price: £50 (52 days, 74.3%)

Price range: £50 - £53

Price levels: 2 different prices over 70 days

Description

Sequential decision making, commonly formalized as Markov Decision Process (MDP) optimization, is an important challenge in artificial intelligence. Two key approaches to this problem are reinforcement learning (RL) and planning. This monograph surveys an integration of both fields, better known as model-based reinforcement learning. Model-based RL has two main steps: dynamics model learning and planning-learning integration. In this comprehensive survey of the topic, the authors first cover dynamics model learning, including challenges such as dealing with stochasticity, uncertainty, partial observability, and temporal abstraction. They then present a systematic categorization of planning-learning integration, including aspects such as: where to start planning, what budgets to allocate to planning and real data collection, how to plan, and how to integrate planning in the learning and acting loop. In conclusion the authors discuss implicit model-based RL as an end-to-end alternative for model learning and planning, and cover the potential benefits of model-based RL. Along the way, the authors draw connections to several related RL fields, including hierarchical RL and transfer learning. This monograph contains a broad conceptual overview of the combination of planning and learning for Markov Decision Process optimization. It provides a clear and complete introduction to the topic for students and researchers alike.

Product Specifications

Format: paperback
ASIN: 1638280568
Category: Books > Subjects > Computing & Internet > Computer Science > Architecture & Microprocessors
Domain: Amazon UK
Release Date: 04 January 2023
Listed Since: 09 January 2023

Barcode

No barcode data available

Model-based Reinforcement Learning: A Survey (Foundations and Trends® in Machine Learning)

Price History & Forecast

Price Distribution

Price Analysis

Description

Product Specifications

Barcode

Similar Products You Might Like

Reinforcement Learning for Sequential Decision and Optimal Control

Reinforcement Learning for Decision & Optimal Control - Springer

Reinforcement Learning and Stochastic Optimization: A Unified Framework for Sequential Decisions

An Introduction to Deep Reinforcement Learning (Foundations and Trends (R) in Machine Learning)

Model-Based Deep Learning (Foundations and Trends® in Signal Processing)

Statistical Reinforcement Learning: Modern Machine Learning Approaches (Chapman & Hall/CRC Machine Learning & Pattern Recognition)

Multi-Agent Reinforcement Learning: Foundations and Modern Approaches

Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning: 55 (Operations Research/Computer Science Interfaces Series, 55)

Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning: 55 (Operations Research/Computer Science Interfaces Series, 55)

Reinforcement Learning and Dynamic Programming Using Function Approximators: 39 (Automation and Control Engineering)

Springer Reinforcement Learning for Optimal Feedback Control

Springer - Reinforcement Learning for Optimal Feedback Control

Reinforcement and Systemic Machine Learning for Decision Making (IEEE Press Series on Systems Science and Engineering)

Human-Robot Interaction Control Using Reinforcement Learning (IEEE Press Series on Systems Science and Engineering)

Deep Reinforcement Learning and Its Industrial Use Cases: AI for Real-World Applications

An Algorithmic Perspective on Imitation Learning (Foundations and Trends in Robotics)

Multi-Agent Coordination: A Reinforcement Learning Approach (IEEE Press)

Reinforcement Learning for Cyber Operations: Applications of Artificial Intelligence for Penetration Testing

The Art of Reinforcement Learning: Fundamentals, Mathematics, and Implementations with Python

Reinforcement Learning: Optimal Feedback Control with Industrial Applications (Advances in Industrial Control)

Decision Making: Uncertainty, Imperfection, Deliberation and Scalability: 538 (Studies in Computational Intelligence, 538)

Stochastic Optimization Methods for Policy Evaluation in Reinforcement Learning (Foundations and Trends® in Optimization)

Neural Approaches to Conversational AI: Question Answering, Task-oriented Dialogues and Social Chatbots (Foundations and Trends® in Information Retrieval)

Deep Reinforcement Learning: Fundamentals, Research and Applications