Prioritized sweeping: Reinforcement learning with less data and less time
نویسندگان
چکیده
منابع مشابه
Prioritized Sweeping: Reinforcement Learning with Less Data and Less Real Time
We present a new algorithm, Prioritized Sweeping, for e cient prediction and control of stochastic Markov systems. Incremental learning methods such as Temporal Di erencing and Qlearning have fast real time performance. Classical methods are slower, but more accurate, because they make full use of the observations. Prioritized Sweeping aims for the best of both worlds. It uses all previous expe...
متن کاملMemory-Based Reinforcement Learning: Efficient Computation with Prioritized Sweeping
[email protected] NE43-771 MIT AI Lab. 545 Technology Square Cambridge MA 02139 We present a new algorithm, Prioritized Sweeping, for efficient prediction and control of stochastic Markov systems. Incremental learning methods such as Temporal Differencing and Q-Iearning have fast real time performance. Classical methods are slower, but more accurate, because they make full use of the observations....
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In this paper, prioritized sweeping confidence based dual reinforcement learning based adaptive network routing is investigated. Shortest Path routing is always not suitable for any wireless mobile network as in high traffic conditions, shortest path will always select the shortest path which is in terms of number of hops, between source and destination thus generating more congestion. In prior...
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Deep reinforcement learning dialogue systems are attractive because they can jointly learn their feature representations and policies without manual feature engineering. But its application is challenging due to slow learning. We propose a two-stage method for accelerating the induction of single or multi-domain dialogue policies. While the first stage reduces the amount of weight updates over ...
متن کاملMasters Thesis: Memory-based Modeling and Prioritized Sweeping in Reinforcement Learning
Reinforcement Learning (RL) is a popular method in machine learning. In RL, an agent learns a policy by observing state-transitions and receiving feedback in the form of a reward signal. The learning problem can be solved by interaction with the system only, without prior knowledge of that system. However, real-time learning from interaction with the system only, leads to slow learning as every...
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ژورنال
عنوان ژورنال: Machine Learning
سال: 1993
ISSN: 0885-6125,1573-0565
DOI: 10.1007/bf00993104