scholarly journals Multi-Agent Deep Reinforcement Learning for Online 3D Human Poses Estimation

2021 ◽  
Vol 13 (19) ◽  
pp. 3995
Author(s):  
Zhen Fan ◽  
Xiu Li ◽  
Yipeng Li
Keyword(s):  
Reinforcement Learning ◽  
Pose Estimation ◽  
Fixed Number ◽  
Agent Based ◽  
View Selection ◽  
Learning Framework ◽  
Multi Agent ◽  
Identical Number ◽  
Human Pose ◽  
3D Information

Most multi-view based human pose estimation techniques assume the cameras are fixed. While in dynamic scenes, the cameras should be able to move and seek the best views to avoid occlusions and extract 3D information of the target collaboratively. In this paper, we address the problem of online view selection for a fixed number of cameras to estimate multi-person 3D poses actively. The proposed method exploits a distributed multi-agent based deep reinforcement learning framework, where each camera is modeled as an agent, to optimize the action of all the cameras. An inter-agent communication protocol was developed to transfer the cameras’ relative positions between agents for better collaboration. Experiments on the Panoptic dataset show that our method outperforms other view selection methods by a large margin given an identical number of cameras. To the best of our knowledge, our method is the first to address online active multi-view 3D pose estimation with multi-agent reinforcement learning.

scholarly journals DTDE: A new cooperative multi-agent reinforcement learning framework

2021 ◽  
pp. 100162
Author(s):  
Guanghui Wen ◽  
Junjie Fu ◽  
Pengcheng Dai ◽  
Jialing Zhou
Keyword(s):  
Reinforcement Learning ◽  
Learning Framework ◽  
Multi Agent

scholarly journals Turbo Learning Framework for Human-Object Interactions Recognition and Human Pose Estimation

2019 ◽  
Vol 33 ◽  
pp. 898-905 ◽  
Author(s):  
Wei Feng ◽  
Wentao Liu ◽  
Tong Li ◽  
Jing Peng ◽  
Chen Qian ◽  
...  
Keyword(s):  
Pose Estimation ◽  
Message Passing ◽  
Closed Loop ◽  
State Of The Art ◽  
Human Action ◽  
Complementary Information ◽  
Learning Framework ◽  
Human Object ◽  
Human Pose ◽  
Object Interactions

Human-object interactions (HOI) recognition and pose estimation are two closely related tasks. Human pose is an essential cue for recognizing actions and localizing the interacted objects. Meanwhile, human action and their interacted objects’ localizations provide guidance for pose estimation. In this paper, we propose a turbo learning framework to perform HOI recognition and pose estimation simultaneously. First, two modules are designed to enforce message passing between the tasks, i.e. pose aware HOI recognition module and HOI guided pose estimation module. Then, these two modules form a closed loop to utilize the complementary information iteratively, which can be trained in an end-to-end manner. The proposed method achieves the state-of-the-art performance on two public benchmarks including Verbs in COCO (V-COCO) and HICO-DET datasets.

scholarly journals Reinforcement Learning under Threats

2019 ◽  
Vol 33 ◽  
pp. 9939-9940 ◽  
Author(s):  
Victor Gallego ◽  
Roi Naveiro ◽  
David Rios Insua
Keyword(s):  
Reinforcement Learning ◽  
Single Agent ◽  
Potential Threat ◽  
Q Learning ◽  
Learning Framework ◽  
Opponent Modeling ◽  
Theoretical Approaches ◽  
New Learning ◽  
Markov Decision ◽  
Multi Agent

In several reinforcement learning (RL) scenarios, mainly in security settings, there may be adversaries trying to interfere with the reward generating process. However, when non-stationary environments as such are considered, Q-learning leads to suboptimal results (Busoniu, Babuska, and De Schutter 2010). Previous game-theoretical approaches to this problem have focused on modeling the whole multi-agent system as a game. Instead, we shall face the problem of prescribing decisions to a single agent (the supported decision maker, DM) against a potential threat model (the adversary). We augment the MDP to account for this threat, introducing Threatened Markov Decision Processes (TMDPs). Furthermore, we propose a level-k thinking scheme resulting in a new learning framework to deal with TMDPs. We empirically test our framework, showing the benefits of opponent modeling.

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