A Temporal Difference GNG-Based Algorithm That Can Learn to Control in Reinforcement Learning Environments

Vieira, Davi C. L.; Adeodato, Paulo J. L.; Gonçalvès, Paulo

doi:10.1109/icmla.2013.67

Cited by 2 publications

(2 citation statements)

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“…Due to high-dimensional and real-valued state spaces, it is usually not feasible to learn a suitable selection policy for each state individually. Aggregation algorithms, e.g., [9,10,6,13,1], dynamically partition the state space of a reinforcement learning problem into disjunct macro states do deal with this problem. Typically, these algorithms start with a coarse-grained partitioning of the state space and refine the state space based on various conditions.…”

Section: Aggregated State Spacesmentioning

confidence: 99%

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Dynamic State Space Partitioning for Adaptive Simulation Algorithms

Helms

Mentel

Uhrmacher

2016

Proceedings of the 9th EAI International Conference on Performance Evaluation Methodologies and Tools

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Adaptive simulation algorithms can automatically change their configuration during runtime to adapt to changing computational demands of a simulation, e.g., triggered by a changing number of model entities or the execution of a rare event. These algorithms can improve the performance of simulations. They can also reduce the configuration effort of the user. By using such algorithms with machine learning techniques, the advantages come with a cost, i.e., the algorithm needs time to learn good adaptation policies and it must be equipped with the ability to observe its environment. An important challenge is to partition the observations to suitable macro states to improve the effectiveness and efficiency of the learning algorithm. Typically, aggregation algorithms, e.g., the adaptive vector quantization algorithm (AVQ), that dynamically partition the state space during runtime are preferred here. In this paper, we integrate the AVQ into an adaptive simulation algorithm.

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Section: Aggregated State Spacesmentioning

confidence: 99%

“…Another group of aggregation algorithms uses the idea of the nearest neighbor vector quantization to create macro states, e.g., [6,13]. These algorithms maintain a codebook CB ⊆ S containing specific states that are called codewords.…”

Section: Aggregated State Spacesmentioning

confidence: 99%