Stable reinforcement learning with recurrent neural networks
β Scribed by James Nate Knight; Charles Anderson
- Book ID
- 107504867
- Publisher
- South China University of Technology and Academy of Mathematics and Systems Science, CAS
- Year
- 2011
- Tongue
- English
- Weight
- 375 KB
- Volume
- 9
- Category
- Article
- ISSN
- 1672-6340
No coin nor oath required. For personal study only.
π SIMILAR VOLUMES
Max and min operations have interesting properties that facilitate the exchange of information between the symbolic and real-valued domains. As such, neural networks that employ max-min activation functions have been a subject of interest in recent years. Since max-min functions are not strictly dif
RAM-based neural networks are designed to be efficiently implemented in hardware. The desire to retain this property influences the training algorithms used, and has led to the use of reinforcement (reward-penalty) learning. An analysis of the reinforcement algorithm applied to RAM-based nodes has s