ArticleCognitive neurodynamics2017
Route searching based on neural networks and heuristic reinforcement learning.
Article in Cognitive neurodynamics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Design of Intelligent Firefighting and Smart Escape Route Planning System Based on Improved Ant Colony Algorithm.Sensors (Basel, Switzerland) · 2024Article
- A search and rescue robot search method based on flower pollination algorithm and Q-learning fusion algorithm.PloS one · 2023Article
- Design exploration predicts designer creativity: a deep learning approach.Cognitive neurodynamics · 2020Article
- Adaptive sparse coding based on memristive neural network with applications.Cognitive neurodynamics · 2019Article
- Consensus of uncertain multi-agent systems with input delay and disturbances.Cognitive neurodynamics · 2019Article
Corrections and comments
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Authors and funding
3 authors.
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Abstract
In this paper, an improved and much stronger RNH-QL method based on RBF network and heuristic Q-learning was put forward for route searching in a larger state space. Firstly, it solves the problem of inefficiency of reinforcement learning if a given problem's state space is increased and there is a lack of prior information on the environment. Secondly, RBF network as weight updating rule, reward shaping can give an additional feedback to the agent in some intermediate states, which will help to guide the agent towards the goal state in a more controlled fashion. Meanwhile, with the process of Q-learning, it is accessible to the underlying dynamic knowledge, instead of the need of background knowledge of an upper level RBF network. Thirdly, it improves the learning efficiency by incorporating the greedy exploitation strategy to train the neural network, which has been testified by the experimental results.
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Registered trials
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