reinforcement learning in neural networks: a survey
نویسندگان
چکیده
in recent years, researches on reinforcement learning (rl) have focused on bridging the gap between adaptive optimal control and bio-inspired learning techniques. neural network reinforcement learning (nnrl) is among the most popular algorithms in the rl framework. the advantage of using neural networks enables the rl to search for optimal policies more efficiently in several real-life applications. although many surveys investigated general rl, no survey is specifically dedicated to the combination of artificial neural networks and rl. this paper therefore describes the state of the art of nnrl algorithms, with a focus on robotics applications. in this paper, a comprehensive survey is started with a discussion on the concepts of rl. then, a review of several different nnrl algorithms is presented. afterwards, the performances of different nnrl algorithms are evaluated and compared in learning prediction and learning control tasks from an empirical aspect and the paper concludes with a discussion on open issues.
منابع مشابه
Reinforcement Learning in Neural Networks: A Survey
In recent years, researches on reinforcement learning (RL) have focused on bridging the gap between adaptive optimal control and bio-inspired learning techniques. Neural network reinforcement learning (NNRL) is among the most popular algorithms in the RL framework. The advantage of using neural networks enables the RL to search for optimal policies more efficiently in several real-life applicat...
متن کاملReinforcement Learning in Neural Networks: A Survey
In recent years, researches on reinforcement learning (RL) have focused on bridging the gap between adaptive optimal control and bio-inspired learning techniques. Neural network reinforcement learning (NNRL) is among the most popular algorithms in the RL framework. The advantage of using neural networks enables the RL to search for optimal policies more efficiently in several real-life applicat...
متن کاملReinforcement Learning in Neural Networks: a Survey
In recent years, researches on reinforcement learning (RL) have focused on bridging the gap between adaptive optimal control and bio-inspired learning techniques. Neural network reinforcement learning (NNRL) is among the most popular algorithms in the RL framework. The advantage of using neural networks enables the RL to search for optimal policies more efficiently in several real-life applicat...
متن کاملUnsupervised and reinforcement learning in neural networks
2.3. Initialize both Q-values at 2 (optimistic). Assume that, as in in the first part, in the first round you get for both actions the reward. Update your Q values once with η = 0.2. Suppose now that in the following rounds, you choose actions a1 and a2 alternatingly and update the Q-values with a very small learning rate (η = 0.001). How many rounds does it take on average, until the maximal Q...
متن کاملLearning in linear neural networks: a survey
Networks of linear units are the simplest kind of networks, where the basic questions related to learning, generalization, and self-organization can sometimes be answered analytically. We survey most of the known results on linear networks, including: 1) backpropagation learning and the structure of the error function landscape, 2) the temporal evolution of generalization, and 3) unsupervised l...
متن کاملReinforcement Learning: A Survey
This paper surveys the eld of reinforcement learning from a computer-science perspective. It is written to be accessible to researchers familiar with machine learning. Both the historical basis of the eld and a broad selection of current work are summarized. Reinforcement learning is the problem faced by an agent that learns behavior through trial-and-error interactions with a dynamic environme...
متن کاملمنابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
international journal of advanced biological and biomedical researchناشر: casrp publishing company
ISSN 2383-2762
دوره 2
شماره 5 2014
میزبانی شده توسط پلتفرم ابری doprax.com
copyright © 2015-2023