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Robotic Grasping using Deep Reinforcement Learning
[article]
2020
arXiv
pre-print
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We use the double deep Q-learning framework along with a novel Grasp-Q-Network to output grasp probabilities used to learn grasps that maximize the pick success. ...
In this work, we present a deep reinforcement learning based method to solve the problem of robotic grasping using visio-motor feedback. ...
Deep learning has enabled reinforcement learning to be used for decision-making problems such as settings with large dimensional state and action spaces that were once unmanageable. ...
arXiv:2007.04499v1
fatcat:l636nzcmqbd2dfek7djyrchlq4
Unsupervised Learning based Jump-Diffusion Process for Object Tracking in Video Surveillance
2018
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
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from raw videos using reinforcement learning methods.Our method is capable of tracking objects with severe occlusions in crowded scenes and thus recovers the complete trajectories of objects that undergo ...
This paper presents a principled way for dealing with occlusions in visual tracking which is a long-standing issue in computer vision but largely remains unsolved. ...
In the discrete action space, for example, one can set an object to be visible if it can be detected with high confidences, or to be occluded if only object parts are detectable (with high confidences) ...
doi:10.24963/ijcai.2018/702
dblp:conf/ijcai/LiuLT18
fatcat:hhcrvc2xonhdffbuzxbnmg5jaq
Exploration for Countering the Episodic Memory
2022
Computational Intelligence and Neuroscience
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spaces, or both) since most states occur only once in deep reinforcement learning. ...
Reinforcement learning is a prominent computational approach for goal-directed learning and decision making, and exploration plays an important role in improving the agent's performance in reinforcement ...
Deep Q-network (DQN) [2, 3] first attempted to apply reinforcement learning to highdimensional problems by combining Q-learning with deep convolutional neural networks (CNN) as parameterized function ...
doi:10.1155/2022/7286186
pmid:35419049
pmcid:PMC8995543
fatcat:3ocvtntowrh2pp6h5uutmpqnfq
Learning Agents with Prioritization and Parameter Noise in Continuous State and Action Space
[chapter]
2019
Lecture Notes in Computer Science
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We believe these results are a valuable addition to the fast-growing body of results on Reinforcement Learning, more so for continuous state and action space problems. ...
that can lend themselves naturally to reinforcement based algorithms, and have continuous state and action spaces. ...
DEEP REINFORCEMENT LEARNING The advent of deep learning has had a significant impact on many areas in machine learning, dramatically improving the state-of-the-art tasks such as object detection, speech ...
doi:10.1007/978-3-030-22796-8_22
fatcat:gahibb7yh5fdtglkoow7htapfu
Poisoning Deep Reinforcement Learning Agents with In-Distribution Triggers
[article]
2021
arXiv
pre-print
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We outline a simple procedure for embedding these, and other, triggers in deep reinforcement learning agents following a multi-task learning paradigm, and demonstrate in three common reinforcement learning ...
We believe that this work has important implications for the security of deep learning models. ...
IN-DISTRIBUTION TRIGGER: PURSUIT For a deep multi-agent reinforcement learning (DMARL) example, we consider the Pursuit environment from the PettingZoo library (Terry et al., 2020a), which consists of ...
arXiv:2106.07798v1
fatcat:gsplz2ghffc4dbu5xbkyzajqwa
Learning Synergies between Pushing and Grasping with Self-supervised Deep Reinforcement Learning
[article]
2018
arXiv
pre-print
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Skilled robotic manipulation benefits from complex synergies between non-prehensile (e.g. pushing) and prehensile (e.g. grasping) actions: pushing can help rearrange cluttered objects to make space for ...
In this work, we demonstrate that it is possible to discover and learn these synergies from scratch through model-free deep reinforcement learning. ...
Our method is based on a pixel-wise version of deep networks that combines deep reinforcement learning with affordance-based manipulation. ...
arXiv:1803.09956v3
fatcat:bqotl6d6xvftzfmm7mxe7mx52q
Deep Reinforcement Learning Based Robot Arm Manipulation with Efficient Training Data through Simulation
[article]
2019
arXiv
pre-print
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Deep reinforcement learning trains neural networks using experiences sampled from the replay buffer, which is commonly updated at each time step. ...
The policy trained with our method works better than the one with the common replay buffer update method. The result is demonstrated both by simulation and by experiment with a real robot arm. ...
Deep reinforcement learning (DRL), which is the conjunction of deep neural network (DNN) [3] and RL, approximates non-linear multidimensional functions by parameterizing agents' experiences through finite ...
arXiv:1907.06884v2
fatcat:2doux7r4rzcq7gv5yt7sl2stdi
Teaching a Robot to Walk Using Reinforcement Learning
[article]
2021
arXiv
pre-print
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Deep Q-learning did not yield a high reward policy, often prematurely converging to suboptimal local maxima likely due to the coarsely discretized action space. ...
Instead, reinforcement learning can train optimal walking policies with ease. ...
This works well for Markov decision process (MDP) problems with discrete state and action spaces, however is insufficient for problems with large continuous state and action spaces. ...
arXiv:2112.07031v1
fatcat:iilpd5wlazf7piwqqxrh4dk6ca
Automated Gain Control Through Deep Reinforcement Learning for Downstream Radar Object Detection
[article]
2021
arXiv
pre-print
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This paper proposes the use of deep reinforcement learning (RL) to learn policies for gain control under the object detection task. ...
This approach allows for simulation of vast amounts of data with flexible assignment of the radar parameters to aid in the active learning process. ...
Deep learning methods for object detection in computer vision can be used for target detection in range-Doppler images [6] , [7] , [8] . ...
arXiv:2107.03792v1
fatcat:ebf6l6bwsvhcpl7agz5rood7ke
Shaping the Future through Innovations: From Medical Imaging to Precision Medicine
[article]
2016
arXiv
pre-print
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image understanding, support for minimally invasive procedures, and patient-specific computational models with enhanced predictive power. ...
This manuscript describes recently developed technologies for better handling of image information: photorealistic visualization of medical images with Cinematic Rendering, artificial agents for in-depth ...
We would also like to thank our clinical and non-clinical collaborators for the strong support over many years.
Disclaimer This feature is based on research, and is not commercially available. ...
arXiv:1605.02029v2
fatcat:kwsyfx5jqrfuhlblnvnd4pf2n4
Learning Synergies Between Pushing and Grasping with Self-Supervised Deep Reinforcement Learning
2018
2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
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Our method is based on a pixel-wise version of deep networks that combines deep reinforcement learning with affordance-based manipulation. ...
Naively weighting actions based on visit counts can also be inefficient due our pixel-wise parameterization of the action space. ...
doi:10.1109/iros.2018.8593986
dblp:conf/iros/ZengSWLRF18
fatcat:y5jktujbqnf75fmwdr7buhrwum
Vision-Based Robotic Arm Control Algorithm Using Deep Reinforcement Learning for Autonomous Objects Grasping
2021
Applied Sciences
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This study aims to evolve the grasping task by reaching the intended object based on deep reinforcement learning. ...
You Only Look Once v5 is employed for object detection, and backward projection is used to detect the three-dimensional position of the target. ...
Acknowledgments: We would like to thank the anonymous referees for their valuable comments and helpful suggestions. ...
doi:10.3390/app11177917
fatcat:ofao3kfp3vhi3bxjhkabvz5xci
Analyzing the Hidden Activations of Deep Policy Networks: Why Representation Matters
[article]
2021
arXiv
pre-print
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We analyze the hidden activations of neural network policies of deep reinforcement learning (RL) agents and show, empirically, that it's possible to know a priori if a state representation will lend itself ...
The results from this analysis provide three main insights into how deep RL agents learn. ...
For a visual example of this training process, see Figure 1 , below.
Learning to pick up objects with deep reinforcement learning We deploy all experiments within two distinct tasks. ...
arXiv:2103.06398v1
fatcat:ew3bkuxujbbsveme7pfx433d6a
Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation
[article]
2016
arXiv
pre-print
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Learning goal-directed behavior in environments with sparse feedback is a major challenge for reinforcement learning algorithms. ...
We present hierarchical-DQN (h-DQN), a framework to integrate hierarchical value functions, operating at different temporal scales, with intrinsically motivated deep reinforcement learning. ...
We are grateful to receive support from the Center for Brain, Machines and Minds (NSF STC award CCF -1231216) and the MIT OpenMind team. ...
arXiv:1604.06057v2
fatcat:p33suojusrcpfpg4ybc4hrfj6y
Bingham Policy Parameterization for 3D Rotations in Reinforcement Learning
[article]
2022
arXiv
pre-print
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We propose a new policy parameterization for representing 3D rotations during reinforcement learning. ...
Today in the continuous control reinforcement learning literature, many stochastic policy parameterizations are Gaussian. ...
ACKNOWLEDGMENTS This work was supported by the Hong Kong Centre for Logistics Robotics. ...
arXiv:2202.03957v1
fatcat:jiftyqwrqzex3k4bd5slsv3bje
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