Hierarchical reinforcement learning: A survey and open research challenges
Reinforcement learning (RL) allows an agent to solve sequential decision-making problems
by interacting with an environment in a trial-and-error fashion. When these environments are …
by interacting with an environment in a trial-and-error fashion. When these environments are …
Recent advances in hierarchical reinforcement learning
AG Barto, S Mahadevan - Discrete event dynamic systems, 2003 - Springer
Reinforcement learning is bedeviled by the curse of dimensionality: the number of
parameters to be learned grows exponentially with the size of any compact encoding of a …
parameters to be learned grows exponentially with the size of any compact encoding of a …
Optimization of global production scheduling with deep reinforcement learning
Abstract Industrie 4.0 introduces decentralized, self-organizing and self-learning systems for
production control. At the same time, new machine learning algorithms are getting …
production control. At the same time, new machine learning algorithms are getting …
Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning
Learning, planning, and representing knowledge at multiple levels of temporal abstraction
are key, longstanding challenges for AI. In this paper we consider how these challenges can …
are key, longstanding challenges for AI. In this paper we consider how these challenges can …
[PDF][PDF] Finite-Time Bounds for Fitted Value Iteration.
R Munos, C Szepesvári - Journal of Machine Learning Research, 2008 - jmlr.org
In this paper we develop a theoretical analysis of the performance of sampling-based fitted
value iteration (FVI) to solve infinite state-space, discounted-reward Markovian decision …
value iteration (FVI) to solve infinite state-space, discounted-reward Markovian decision …
Reinforcement learning for predictive maintenance: A systematic technical review
The manufacturing world is subject to ever-increasing cost optimization pressures.
Maintenance adds to cost and disrupts production; optimized maintenance is therefore of …
Maintenance adds to cost and disrupts production; optimized maintenance is therefore of …
Deep reinforcement learning for semiconductor production scheduling
Despite producing tremendous success stories by identifying cat videos [1] or solving
computer as well as board games [2],[3], the adoption of deep learning in the semiconductor …
computer as well as board games [2],[3], the adoption of deep learning in the semiconductor …
[图书][B] Temporal abstraction in reinforcement learning
D Precup - 2000 - search.proquest.com
Decision making usually involves choosing among different courses of action over a broad
range of time scales. For instance, a person planning a trip to a distant location makes high …
range of time scales. For instance, a person planning a trip to a distant location makes high …
Relax: Incorporating uncertainty into the specification of self-adaptive systems
Self-adaptive systems have the capability to autonomously modify their behaviour at run-
time in response to changes in their environment. Self-adaptation is particularly necessary …
time in response to changes in their environment. Self-adaptation is particularly necessary …
System and method for dynamic multi-objective optimization of machine selection, integration and utilization
A Sustaeta, KH Lin, R Snyder, JC Theron… - US Patent …, 2017 - Google Patents
The invention provides control systems and methodologies for controlling a process having
computer-controlled equipment, which provide for optimized process performance according …
computer-controlled equipment, which provide for optimized process performance according …