[PDF] Top 20 Hierarchical Average Reward Reinforcement Learning
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Hierarchical Average Reward Reinforcement Learning
... the average reward setting, and investigate two formulations of HRL based on average reward ...HRL: hierarchical optimality and recursive optimality (Dietterich, ...MAXQ ... See full document
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Combining Hierarchical Reinforcement Learning and Bayesian Networks for Natural Language Generation in Situated Dialogue
... Language generators in situated domains face a number of content selection, utterance plan- ning and surface realisation decisions, which can be strictly interdependent. We there- fore propose to optimise these processes ... See full document
11
Diversity-Driven Extensible Hierarchical Reinforcement Learning
... and MLSH are plotted in Figure 6 (upper part), which al- ways drop when the goal is changed, because the top-level hierarchies of both methods are re-initialized. Obviously, the increase speeds of the episode extrinsic ... See full document
8
Hierarchical Reinforcement Learning for Adaptive Text Generation
... We distinguish three kinds of state representations, displayed in Table 2. The first (M 10 0 and M 0 1 ) en- codes information on the spatial environment and user type so that texts can be tailored towards these ... See full document
9
Hierarchical Reinforcement Learning and Hidden Markov Models for Task Oriented Natural Language Generation
... the one-to-many relationship arising between a se- mantic form (from the content selection stage) and its possible realisations. Semantic forms of instruc- tions have an average of 650 surface realisations, ... See full document
6
Sentence Mover’s Similarity: Automatic Evaluation for Multi Sentence Texts
... a reward when learning a generation model via reinforcement learning; we present both automatic and human evalua- tions of summaries learned in this way, finding that our approach outperforms ... See full document
13
Autonomous Sub domain Modeling for Dialogue Policy with Hierarchical Deep Reinforcement Learning
... To assess the impact of each type of prior knowl- edge, we implemented an HRL-GP framework that uses both types of knowledge and its vari- ant HRL-GP2 that uses only sub-domain infor- mation. Both frameworks have ... See full document
8
Composite Task Completion Dialogue Policy Learning via Hierarchical Deep Reinforcement Learning
... the reward sparsity issue, we equip our agent with an evaluation module (internal critic) that gives in- trinsic reward signals, indicating how likely a par- ticular subtask is completed based on its ... See full document
10
Interactive Semantic Parsing for If-Then Recipes via Hierarchical Reinforcement Learning
... a Hierarchical Reinforce- ment Learning (HRL) ...a hierarchical policy: A high-level policy decides the order of the subtasks to work on, and a low-level policy for each subtask guides its com- ... See full document
8
Hierarchical Reinforcement Learning for Course Recommendation in MOOCs
... Nowadays, massive open online courses, or MOOCs, are attracting widespread interest as an alternative education model. Lots of MOOCs platforms such as Coursera, edX and Udacity have been built and provide low cost ... See full document
8
The Dopaminergic Midbrain Mediates an Effect of Average Reward on Pavlovian Vigor
... baseline reward of the subsequent block n + 1 were also included at start of block n, plus a stick function regressor indicating when an error response ...baseline reward condi- tion of the current block ... See full document
16
Study of Human Hand-Eye Coordination Using Machine Learning Techniques in a Virtual Reality Setup
... inverse reinforcement learning framework was used to visualize different strategies through an interpretation of recov- ered reward values associated with different ...its reward modules ... See full document
160
Reward Balancing for Statistical Spoken Dialogue Systems using Multi objective Reinforcement Learning
... Reinforcement learning is widely used for dialogue policy optimization where the re- ward function often consists of more than one component, ...optimal reward com- ponent ...ment learning to ... See full document
6
Leveraging Hierarchical Category Knowledge for Data Imbalanced Multi Label Diagnostic Text Understanding
... that learning low-level labels directly is difficult due to the highly imbalanced label dis- tribution, we add a loss term indicating the high- level category in order to learn the general con- cepts in addition ... See full document
5
Complexity Weighted Loss and Diverse Reranking for Sentence Simplification
... a reinforcement learning frame- work at training time to reward the model for pro- ducing sentences that score high on fluency, ad- equacy, and ... See full document
11
What is Acceptably Safe for Reinforcement Learning?
... AlphaGo’s learning had moved beyond the limits of what the developers were able to comprehend, which brings about an interesting question when considering the safety assurance of RL Systems; do we need to consider ... See full document
14
Learning the Variance of the Reward-To-Go
... the reward-to-go is a natural measure of uncertainty about the long term performance of a policy, and is important in domains such as finance, resource allocation, and process ...expected reward-to-go, also ... See full document
36
Macquarie University at BioASQ 6b: Deep learning and deep reinforcement learning for query based summarisation
... samples an action from the current global policy plus some perturbation p (line 7) and applies the action (line 11). When all the candidate sentences related to the question have been processed and ac- tioned on (line ... See full document
8
Load Balancing in Heterogeneous Network Using Machine Learning Technique
... A reinforcement learning algorithm is proposed with variance is service rate as the reward function and by considering the problem as N-arm bandit problem the load is balanced between the various ... See full document
6
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 ... See full document
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