[PDF] Top 20 Using reinforcement learning to coordinate better
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Using reinforcement learning to coordinate better
... of learning to cover other aspects of the agent’s decision framework; such as to learn the decision about how much to bid in a request for coordination (Section ...effective learning objectives, agents ... See full document
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Using reinforcement learning to coordinate better
... The agents move around the grid one step at a time, up, down, left or right, or stay still. At any one time, each agent has a single goal, either its ST or a CT over which coordination needs to be achieved. On arrival at ... See full document
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Transfer in Deep Reinforcement Learning Using Knowledge Graphs
... that using knowledge graphs as a state representation enables effi- cient transfer between deep reinforcement learn- ing agents designed to play text-adventure games, reducing training times and increasing ... See full document
10
Learning to Diagnose: Assimilating Clinical Narratives using Deep Reinforcement Learning
... Overall, the low F-measures demonstrate the difficulty of the task, as they are consistently low for all methods. We use exact sentences from a clinical narrative as queries to search for the diag- noses in the knowledge ... See full document
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Investigations into Playing Chess Endgames using Reinforcement Learning
... The general method used for playing chess in computers is to first generate all the possible legal moves from the current position and then evaluate each of these to find which is best and perform that move. Of course, ... See full document
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Exploration and Exploitation Tradeoff using Fuzzy Reinforcement Learning
... Complexity of interaction between exploration and exploitation in single agent environment is a known problem which different methods provided for its solution. We can classify available solutions in two general classes ... See full document
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A Deep Reinforcement Learning Framework for Rebalancing Dockless Bike Sharing Systems
... ment learning algorithm called the hierarchical reinforce- ment pricing (HRP) ...2015), using the general hierarchical reinforcement learning framework (Dietterich ...a better ... See full document
8
Evolutionary Function Approximation for Reinforcement Learning
... achieved using recency-weighting update rules like those employed by table-based TD ...methods. Using Steady-State Evolutionary Computation The NEAT algorithm used in this paper is an example of ... See full document
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Aircraft Type Identification Using Reinforcement Active Learning
... active learning methods are carefully formulating some criteria for selecting samples, such as uncertainty sampling[7], query-by-committee[8], margin[9] and representative and diversity-based ...is better, ... See full document
7
A Discriminative Learning Model for Coordinate Conjunctions
... in coordinate identification, ...was better when the word and suffix features were re- moved, while the box-based method and CRF chun- kers performed better with these ... See full document
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Learning Mixed Initiative Dialog Strategies By Using Reinforcement Learning On Both Conversants
... To better understand the lack of convergence, we ex- plore when a single weight is chosen for the objec- tive ...the reinforcement learning procedure does not always converge on an optimal ... See full document
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Classification with Costly Features Using Deep Reinforcement Learning
... Another interesting fact is that RL-dqn and the version without HPC perform better on the forest dataset. The situa- tion is even more profound in the case of forest-2 (Figure 4g), where the BudgetPrune also ... See full document
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Determinantal Reinforcement Learning
... machine learning applications, includ- ing recommendation of products (Gillenwater et ...to better represent the negative correlation between neurons (Snoek, Zemel, and Adams ... See full document
8
Decentralized optimization of fluctuating urban traffic using reinforcement learning
... that Soilse using ǫ -greedy outperformed the ases where Boltzmann or greedy were used. We laried the reasons for that whih are mainly due to the eient nature ǫ -greedy exhibits in relearning better poliies. ... See full document
166
Residual Reinforcement Learning using Neural Networks
... The simplest rule when selecting an action is to select the action with the highest estimated action value. This rule is called the greedy method. If an action appears to have a high total reward compared with other ... See full document
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Personalized project recommendations: using reinforcement learning
... For some purposes, when A wants to improve B’s trust value, A will communicate with B frequently, which usu- ally start with B’s interests and hobbies. If B likes watching movies, A will often recommend B the movies she ... See full document
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Primary User Avoidance algorithm for CRAHNs using Reinforcement Learning
... actual learning starts, the environment is observed for some ...after learning, the action will be based on prediction, not on random ...that better performance is ... See full document
7
Using Reinforcement Learning to Build a Better Model of Dialogue State
... This paper presents initial research toward the long-term goal of designing a tutoring system that can effectively adapt to the student. While most work in Markov Decision Processes (MDPs) and spoken dialogue have ... See full document
8
Continual State Representation Learning for Reinforcement Learning using Generative Replay
... evaluation: Learning curves are presented in ...that using State Representation instead of directly using the raw states is superior in terms of final performance and sample ...obtain better ... See full document
9
Early Rumour Detection
... machine learning algorithms (Qazvinian et ...Twitter using cue words and tweets ...with using these propagation patterns extensively to improve ... See full document
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