Reinforcement Learning (Machine Learning)
A STUDY OF REINFORCEMENT LEARNING APPLICATIONS & ITS ALGORITHMS
... of machine learning just like humans and the ability to react to specific ...with machine learning. Machine learning is the developed approach in the field of Artificial ...by ...
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Evolutionary Function Approximation for Reinforcement Learning
... challenging reinforcement learning task called server job ...believe machine learning will play a primary role, since com- puter systems must be adaptive if they are to perform well ...and ...
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Determinantal Reinforcement Learning
... A DPP defines a probability distribution over the subsets from a ground set. The probability of a subset is propor- tional to the determinant of a principal submatrix of a posi- tive semidefinite matrix, where the ...
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Improving Optimization Bounds Using Machine Learning: Decision Diagrams Meet Deep Reinforcement Learning
... deep reinforcement learning for obtaining an ordering for tightening the bounds obtained with relaxed and restricted ...deep reinforcement learning approach, by achieving tighter objec- tive ...
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Reinforcement learning based dynamic band and channel selection in cognitive radio ad hoc networks
... as machine learning, has been attracting attention in vari- ous fields ...the reinforcement learning is being studied in the wireless system field because it pro- vides a solution to optimize ...
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Machine Teaching for Inverse Reinforcement Learning: Algorithms and Applications
... Inverse reinforcement learning (IRL) infers a reward function from demonstrations, allowing for policy improvement and ...a machine teaching problem where the goal is to find the minimum number of ...
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Learning to Teach in Cooperative Multiagent Reinforcement Learning
... inverse reinforcement learning (Ng and Russell 2000), apprenticeship learning (Abbeel and Ng 2004), and learning from demonstration (Argall et ...curriculum learning (Bengio et ...
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Reinforcement Learning for Bandit Neural Machine Translation with Simulated Human Feedback
... for reinforcement learning from human feedback: users provide quick, dirty ratings on candidate translations to guide a system to ...neural machine translation training fo- cuses on expensive ...
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Reinforcement Learning based Curriculum Optimization for Neural Machine Translation
... a reinforcement-learning (RL) approach involving a learned agent whose task is to choose a corpus bin, representing a given noise level, at each NMT training ...
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MODIFIED ACTION VALUE METHOD APPLIED TO ‘n’-ARMED BANDIT PROBLEMS USING REINFORCEMENT LEARNING
... Reinforcement Learning is learning from interactions with an environment, from the consequences of action, rather than from explicit ...Problems. Reinforcement Learning algorithms are ...
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A Study of Reinforcement Learning for Neural Machine Translation
... Recent studies have shown that reinforcemen- t learning (RL) is an effective approach for improving the performance of neural machine translation (NMT) system. However, due to its instability, successfully ...
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Personalized project recommendations: using reinforcement learning
... a reinforcement learning method Deep Q-Learning (DQN) is studied to simulate the process of learning user ’ s preferences and boosting trust ...
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A Deep Reinforcement Learning Framework for Rebalancing Dockless Bike Sharing Systems
... deep reinforcement learn- ing framework for incentivizing users to rebalance such sys- ...deep reinforcement learning al- gorithm called Hierarchical Reinforcement Pricing (HRP), which builds ...
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Experience based Reinforcement Learning to Acquire Effective Behavior in a Multiagent Domain
... the cut-loop routine is applied. Prot-sharing uses trial and error experiences, and reinforces eective rules instead of estimating values for the dierent state. Therefore, it uses this policy to escape states susceptible ...
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Undirected Machine Translation with Discriminative Reinforcement Learning
... Because UMT prunes all but the single cho- sen action at each step, both choosing a good in- ference order and choosing a correct action re- duce to a single choice of what action to take next. To learn this decoding ...
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Transfer Learning for Reinforcement Learning Domains: A Survey
... Transfer learning in RL is an important topic to address at this time for three ...other machine learning techniques are either unable or ill-equipped to address ...classical machine ...
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Improve the Performance of a Complex FMS with a Hybrid Machine Learning Algorithm
... Figure 6 shows that the optimal solution found by the GA for the use case 3 is a repeat chromosome. This means that a product may hinder another product. The optimal solution for the entire process is not a simple ...
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Artificial intelligence as a means to facilitate mechanism design based negotiations
... qualitative learning phase for deep neural networks, as well as instant ...Then, reinforcement learning is integrated into the networks to optimise the supervised learning ...deep ...
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Assessment of Linearity Improvement in Optical Communication Systems with Machine Learning Methods
... comparing Reinforcement Learning (RL) based machine learning method with Support Vector Machine (SVM) method and conventional ...
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Learning to Act with RVRL Agents
... The development of situated agents for stochastic environments presents many challenges to designers of multi-agent systems. If the agent is to use a state- based representation, in which every state it encounters is ...
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