Deep Reinforcement Learning with the Random Neural Network

Author:

Serrano Will

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Artificial Intelligence,Control and Systems Engineering

Reference56 articles.

1. A deep reinforcement learning process based on robotic training to assist mental health patients;Altameem;Neural Comput. Appl.,2020

2. Caicedo, J., Lazebnik, S., 2015. Active Object Localisation with Deep Reinforcement Learning. In: IEEE International Conference on Computer Vision. pp. 1–9.

3. Christiano, P., Leike, J., Brown, T., Martic, M., Legg, S., Amodei, D., 2017. Deep Reinforcement Learning from Human references. In: 31st Conference on Neural Information Processing Systems. pp. 1–17.

4. LSTM training set analysis and clustering model development for short-term traffic flow prediction;Dogan;Neural Comput. Appl.,2021

5. Duan, Y., Chen, X., Houthooft, R., Schulman, J., Abbeel, P., 2016. Benchmarking deep reinforcement learning for continuous control. In: International Conference on Machine Learning. pp. 1329–1338.

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