A Reinforcement Learning Approach to Lift Generation in Flapping MAVs: Experimental Results

2007 ◽  
Author(s):  
Mehran Motamed ◽  
Joseph Yan
Keyword(s):  
Reinforcement Learning ◽  
Experimental Results ◽  
Learning Approach

scholarly journals Deep Reinforcement Learning for Query-Conditioned Video Summarization

2019 ◽  
Vol 9 (4) ◽  
pp. 750 ◽  
Author(s):  
Yujia Zhang ◽  
Michael Kampffmeyer ◽  
Xiaoguang Zhao ◽  
Min Tan
Keyword(s):  
Reinforcement Learning ◽  
Visual Information ◽  
Video Summarization ◽  
Experimental Results ◽  
Learning Approach ◽  
Video Content ◽  
User Interests ◽  
User Query ◽  
Mapping Mechanism

Query-conditioned video summarization requires to (1) find a diverse set of video shots/frames that are representative for the whole video, and that (2) the selected shots/frames are related to a given query. Thus it can be tailored to different user interests leading to a better personalized summary and differs from the generic video summarization which only focuses on video content. Our work targets this query-conditioned video summarization task, by first proposing a Mapping Network (MapNet) in order to express how related a shot is to a given query. MapNet helps establish the relation between the two different modalities (videos and query), which allows mapping of visual information to query space. After that, a deep reinforcement learning-based summarization network (SummNet) is developed to provide personalized summaries by integrating relatedness, representativeness and diversity rewards. These rewards jointly guide the agent to select the most representative and diversity video shots that are most related to the user query. Experimental results on a query-conditioned video summarization benchmark demonstrate the effectiveness of our proposed method, indicating the usefulness of the proposed mapping mechanism as well as the reinforcement learning approach.

scholarly journals A Deep Reinforcement Learning Approach to The Ancient Indian Game - Chowka Bhara

2021 ◽  
Author(s):  
Annapurna P Patil ◽  
SANJAY RAGHAVENDRA ◽  
Shruthi Srinarasi ◽  
Reshma Ram
Keyword(s):  
Artificial Intelligence ◽  
Reinforcement Learning ◽  
Experimental Results ◽  
Learning Approach ◽  
Board Game ◽  
Q Learning

<p>Reinforcement Learning (RL) is the study of how Artificial Intelligence (AI) agents learn to make their own decisions in an environment to maximize the cumulative reward received. Although there has been notable progress in the application of RL for games, the category of ancient Indian games has remained almost untouched. Chowka Bhara is one such ancient Indian board game. This work aims at developing a Q-Learning-based RL Chowka Bhara player whose strategies and methodologies are obtained from three Strategic Players viz. Fast Player, Random Player, and Balanced Player. It is observed through the experimental results that the Q-Learning Player outperforms all three Strategic Players.</p>

scholarly journals A Deep Reinforcement Learning Approach to The Ancient Indian Game - Chowka Bhara

2021 ◽  
Author(s):  
Annapurna P Patil ◽  
SANJAY RAGHAVENDRA ◽  
Shruthi Srinarasi ◽  
Reshma Ram
Keyword(s):  
Artificial Intelligence ◽  
Reinforcement Learning ◽  
Experimental Results ◽  
Learning Approach ◽  
Board Game ◽  
Q Learning

<p>Reinforcement Learning (RL) is the study of how Artificial Intelligence (AI) agents learn to make their own decisions in an environment to maximize the cumulative reward received. Although there has been notable progress in the application of RL for games, the category of ancient Indian games has remained almost untouched. Chowka Bhara is one such ancient Indian board game. This work aims at developing a Q-Learning-based RL Chowka Bhara player whose strategies and methodologies are obtained from three Strategic Players viz. Fast Player, Random Player, and Balanced Player. It is observed through the experimental results that the Q-Learning Player outperforms all three Strategic Players.</p>

Energy-efficient UAV trajectory design for backscatter communication: A deep reinforcement learning approach

2020 ◽  
Vol 17 (10) ◽  
pp. 129-141
Author(s):  
Yiwen Nie ◽  
Junhui Zhao ◽  
Jun Liu ◽  
Jing Jiang ◽  
Ruijin Ding
Keyword(s):  
Reinforcement Learning ◽  
Energy Efficient ◽  
Learning Approach ◽  
Trajectory Design ◽  
Backscatter Communication

Attentional Shifting and Curiosity: a Reinforcement Learning Approach

2016 ◽  
Author(s):  
Dario di Nocera ◽  
Alberto Finzi ◽  
Silvia Rossi ◽  
Mariacarla Staffa
Keyword(s):  
Reinforcement Learning ◽  
Learning Approach ◽  
Attentional Shifting
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