reward function
Towards Safe Artificial General Intelligence
... utility function ˙ u is virtually eradicated by the exemplified ...crafted reward function that con- siders both the performance and the integrity of the ...
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Towards an interactive drone : a Bayesian optimization approach
... It can be said though that, if the interest is in generalizing knowledge, it is more beneficial to use the results of training for the harder task. For example, it can be seen that the parameters obtained from the square ...
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Dialogue Strategy Learning in Healthcare: A Systematic Approach for Learning Dialogue Models from Data
... We aim to build dialogue agents that op- timize the dialogue strategy, specifically through learning the dialogue model com- ponents from dialogue data. In this paper, we describe our current research on au- tomatically ...
7
Load Balancing in Heterogeneous Network Using Machine Learning Technique
... A policy maps the actions to be taken for the perceived states of the environment [14]. Let S be the state space and A be the action space, a policy π (s, a) can be defined as the probability of choosing action a in ...
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Reactive control of a two-body point absorber using reinforcement learning
... The authors have presented an on-line, model free strategy for the reactive control of WECs using RL, building on a previous study on resistive control. The algorithm has been validated through a numerical model of a ...
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Multi-Objective Markov Decision Processes for Data-Driven Decision Support
... value function approximation to accommo- date continuous state features, thus allowing us to use the MOMDP framework to analyze continuous-valued sequential ...“true reward function” that is linear ...
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What Should I Ask? Using Conversationally Informative Rewards for Goal oriented Visual Dialog
... valuable reward function is a crucial aspect for any Reinforcement Learning ...good reward function for asking goal-oriented ...the reward func- tion should help the questioner achieve ...
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Staying Alive: System Design for Self-Sufficient Sensor Networks
... r(u) corresponds to the maximum achievable throughput for the given multihop net- work. In Figure 7, we show results for dense, medium, and sparse networks (rep- resented with squares, circles, and triangles, ...
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A Reward Functional to Solve the Replacement Problem
... In this document, a stochastic machine replacement model is considered. The system consists of a single machine and this is assumed to operate continuously and efficiently over N periods. In each period, the quality of ...
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Approximate Dynamic Oracle for Dependency Parsing with Reinforcement Learning
... present reward function is not necessarily ...the reward function in order to demonstrate that the ADO is able to learn from delayed feedback information, which is necessary in most systems ...
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Hierarchical Reinforcement Learning and Hidden Markov Models for Task Oriented Natural Language Generation
... the reward function is arguably the agent’s most crucial ...a reward function from human data as in the PARADISE framework (Walker et ...a reward function from human data, ...
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Combining Hierarchical Reinforcement Learning and Bayesian Networks for Natural Language Generation in Situated Dialogue
... weighted function of task success and dia- logue cost measures ...performance function P erf ormance = ...this reward function (and − 1 for each other action), the agent is rewarded for short ...
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Learning to Interpret Natural Language Instructions
... Inverse Reinforcement Learning. Inverse Re- inforcement Learning (Abbeel and Ng, 2004) ad- dresses the task of learning a reward function from demonstrations of expert behavior and information about the ...
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Neural Keyphrase Generation via Reinforcement Learning with Adaptive Rewards
... Generating keyphrases that summarize the main points of a document is a fundamen- tal task in natural language processing. Al- though existing generative models are capa- ble of predicting multiple keyphrases for an ...
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Exploring Deep Reinforcement Learning with Multi Q Learning
... When the standard deviation of the reward function was increased, the deviation in the value estimate increased for each algorithm. When using an 𝜀𝜀-greedy behavior pol- icy, Q-learning and Double ...
16
Reward, learning and games
... identified reward as an area where new scientific insights might inform educational understanding and improve classroom ...consider reward to include both material and social reinforcers, and motivation as ...
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Literacy And Reward: Teachers’ Effort To Build Children Reading Habit
... the reward variables (intangible and tangible rewards (prizes of token, certificate, food), reward expectancy) significantly improved the prediction of post-reward intrinsic reading motivation, ...
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Sexual Reward and Depression
... In addition to mediating energy homeostasis, orexin is an important mediator of motivation and reward associated with feeding. ICV orexin-A and orexin-B infusion stimulates feeding (Sakurai et al., 1998) and IP ...
226
Correlating nurses’ levels of Psychological Capital with their reward preferences and reward satisfaction
... experience reward satisfaction they are more likely to have an expectancy of success, which stems from having optimism, and a belief in their personal abilities, which is derived from self-efficacy and ...
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Measurement Maximizing Adaptive Sampling with Risk Bounding Functions
... expected reward of samples, while limiting the probability of failure of the ...of reward through a risk bounding function, and en- force the chance constraint that the expected rate of failure is ...
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