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Deterministic Policy Gradient Based Robotic Path Planning with Continuous Action Spaces

Somdyuti Paul, Lovekesh Vig
2017 2017 IEEE International Conference on Computer Vision Workshops (ICCVW)  
In this paper, we present a deterministic policy based actor-critic learning framework to encode the path planning strategy irrespective of the robot pose and target object position.  ...  In the episodic learning framework, the actor-critic network learns the optimal actions in the continuous space of real numbers for a given state configuration by trying to increase the expected reward  ...  In this paper, we present a novel approach for robot arm control in the continuous action space using an actor-critic based RL framework trained using deterministic policy gradient.  ... 
doi:10.1109/iccvw.2017.91 dblp:conf/iccvw/PaulV17 fatcat:rmgorj3ka5annmf4o3gsrfo434

Motion Planning of Robot Manipulators for a Smoother Path Using a Twin Delayed Deep Deterministic Policy Gradient with Hindsight Experience Replay

MyeongSeop Kim, Dong-Ki Han, Jae-Han Park, Jung-Su Kim
2020 Applied Sciences  
This paper proposes a motion planning algorithm for robot manipulators using a twin delayed deep deterministic policy gradient (TD3) which is a reinforcement learning algorithm tailored to MDP with continuous  ...  Since path planning for a robot manipulator is an MDP (Markov Decision Process) with sparse reward and HER can deal with such a problem, this paper proposes a motion planning algorithm using TD3 with HER  ...  DDPG is used since the action in RAMDP is a continuous value and DDPG is a policy gradient tailored to an MDP with a continuous action.  ... 
doi:10.3390/app10020575 fatcat:mm7ausygmjdevht4b4n75pwkoa

Combining Deep Reinforcement Learning and Safety Based Control for Autonomous Driving [article]

Xi Xiong, Jianqiang Wang, Fang Zhang, Keqiang Li
2016 arXiv   pre-print
In this passage, we use the Deep Deterministic Policy Gradient algorithm to implement autonomous driving without vehicles around.  ...  With the development of state-of-art deep reinforcement learning, we can efficiently tackle continuous control problems.  ...  ., 2015) presents an actor-critic, model-free algorithm based on the deterministic policy gradient that can operate over continuous action spaces.  ... 
arXiv:1612.00147v1 fatcat:cf3etu4jcra4tarmt5kstezteq

Path Planning for Multi-Arm Manipulators Using Deep Reinforcement Learning: Soft Actor–Critic with Hindsight Experience Replay

Evan Prianto, MyeongSeop Kim, Jae-Han Park, Ji-Hun Bae, Jung-Su Kim
2020 Sensors  
Motivated by this, in this paper, a SAC-based path planning algorithm is proposed.  ...  Especially, when it comes to deep reinforcement learning-based path planning, high-dimensionality makes it difficult for existing reinforcement learning-based methods to have efficient exploration which  ...  ] and TD3 (Twin Delayed Deep Deterministic Policy Gradient)-based path planning [30] .  ... 
doi:10.3390/s20205911 pmid:33086774 pmcid:PMC7590214 fatcat:yludsqd6tza7zak36f6gcpacfi

Deep Reinforcement Learning Based Trajectory Planning Under Uncertain Constraints

Lienhung Chen, Zhongliang Jiang, Long Cheng, Alois C. Knoll, Mingchuan Zhou
2022 Frontiers in Neurorobotics  
Since the robot manipulator operates in high dimensional continuous state-action spaces, model-free, policy gradient-based soft actor-critic (SAC), and deep deterministic policy gradient (DDPG) framework  ...  and discover optimal trajectory planning by interacting with the environment.  ...  Deep Deterministic Policy Gradient Deep deterministic policy gradient (DDPG), introduced in Lillicrap et al. (2015) , is an actor-critic, model-free algorithm based on the deterministic policy gradient  ... 
doi:10.3389/fnbot.2022.883562 pmid:35586262 pmcid:PMC9108367 fatcat:gwp43tyy3jeclcilngjloitizy

A Reinforcement Learning based Path Planning Approach in 3D Environment [article]

Geesara Kulathunga
2022 arXiv   pre-print
One of them is a deterministic tree-based approach and other two approaches are based on Q-learning and approximate policy gradient, respectively.  ...  We analyzed several types of reinforcement learning-based approaches for path planning.  ...  RL-based path planning can be classified in two ways: model-base, model free.  ... 
arXiv:2105.10342v2 fatcat:ddnjtt2unveibd7ne5aju6rbra

Goal-Oriented Obstacle Avoidance with Deep Reinforcement Learning in Continuous Action Space

Reinis Cimurs, Jin Han Lee, Il Hong Suh
2020 Electronics  
The control signals for robot motion are output in a continuous action space.  ...  We devise a deep deterministic policy gradient network with the inclusion of depth-wise separable convolution layers to process the large amounts of sequential depth image information.  ...  Convolutional Deep Deterministic Policy Gradient For effective goal-oriented obstacle avoidance from depth images in a continuous action space, we devised a DDPG-based network by implementing depth-wise  ... 
doi:10.3390/electronics9030411 fatcat:d7f56sq76rebdfr6gnpoa66iza

A survey of learning‐based robot motion planning

Jiankun Wang, Tianyi Zhang, Nachuan Ma, Zhaoting Li, Han Ma, Fei Meng, Max Q.‐H. Meng
2021 IET Cyber-Systems and Robotics  
Recently, learning-based motion-planning methods have shown significant advantages in solving different planning problems in high-dimensional spaces and complex environments.  ...  A fundamental task in robotics is to plan collision-free motions among a set of obstacles.  ...  in continuous action space.  ... 
doi:10.1049/csy2.12020 fatcat:ysj2sjwbgnf7ljukarfqnyftg4

Motion planning for mobile Robots–focusing on deep reinforcement learning: A systematic Review

Huihui Sun, Weijie Zhang, Runxiang YU, Yujie Zhang
2021 IEEE Access  
Mobile robots contributed significantly to the intelligent development of human society, and the motion-planning policy is critical for mobile robots.  ...  INDEX TERMS Mobile robot; Deep reinforcement learning; Motion planning  ...  The mobile robot motion planning with continuous action space can obtain better performance based on Actor-Critic . Jaderberg M et al.  ... 
doi:10.1109/access.2021.3076530 fatcat:53kdh5cfgvcang5xymf4vrqx2e

Integrating a Path Planner and an Adaptive Motion Controller for Navigation in Dynamic Environments

Junjie Zeng, Long Qin, Yue Hu, Quanjun Yin, Cong Hu
2019 Applied Sciences  
Simulated experiments show that compared with existing methods, this JPS-IA3C hierarchy successfully outputs continuous commands to accomplish large-range navigation tasks at shorter paths and less time  ...  We additionally strengthen the robots' temporal reasoning of the environments by a memory-based network.  ...  ., value-based DRL and policy-based DRL). Compared with valued-based DRL methods, policy-based methods are more suitable for us to handle continuous action spaces.  ... 
doi:10.3390/app9071384 fatcat:cqjgcfly6bfnthu7zwxymm6p2i

Hybrid Formation Control for Multi-Robot Hunters Based on Multi-Agent Deep Deterministic Policy Gradient

Oussama Hamed, Mohamed Hamlich
2021 The MENDEL Soft Computing journal : International Conference on Soft Computing MENDEL  
Deep Deterministic Policy Gradient (MADDPG) to plan an optimal accessible path to the desired position.  ...  Hunting a moving target with random behavior is an application that requires robust cooperation between several robots in the multi-robot system.  ...  MADDPG is an extension of DDPG which is proposed to handle continuous state-action spaces and to use a centralized planning with a decentralized execution for each agent [8] .  ... 
doi:10.13164/mendel.2021.2.023 doaj:fedd95128b6e487bb1866d97dd6b9637 fatcat:vmwoxrlqh5hb7oafr6wmxqopwe

TOWARDS CONTINUOUS CONTROL FOR MOBILE ROBOT NAVIGATION: A REINFORCEMENT LEARNING AND SLAM BASED APPROACH

K. A. A. Mustafa, N. Botteghi, B. Sirmacek, M. Poel, S. Stramigioli
2019 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
deterministic policy gradient (DDPG).  ...  </strong> We introduce a new autonomous path planning algorithm for mobile robots for reaching target locations in an unknown environment where the robot relies on its on-board sensors.  ...  for continuous action spaces.  ... 
doi:10.5194/isprs-archives-xlii-2-w13-857-2019 fatcat:a5smoksadraxdjtn3z5r6ueshq

CONTROL & NAVIGATION IN ROBOTS USING REINFORCEMENT LEARNING

Palepu Jithin kumar, Bahurothu Venkata Hemanth, Pallavi Gupta
2020 International Research Journal of Computer Science  
Reinforcement Learning alters with techniques like supervised and unsupervised in such a way that in RL the agent gets up with its own insights and maps what action to perform in certain situations.  ...  RL is used almost everywhere, the best applications of RL in Robotics specifically in motion control, planning it is also used in finance, gaming etc.  ...  Deep Deterministic Policy Gradient: The core algorithm to this robot is a policy gradient algorithm known as Deep Deterministic Policy Gradient.  ... 
doi:10.26562/irjcs.2020.v0709.006 fatcat:wbakhrgoebff5gtvhb2ihho5wi

Efficient Path Planning for Mobile Robot Based on Deep Deterministic Policy Gradient

Hui Gong, Peng Wang, Cui Ni, Nuo Cheng
2022 Sensors  
When a traditional Deep Deterministic Policy Gradient (DDPG) algorithm is used in mobile robot path planning, due to the limited observable environment of mobile robots, the training efficiency of the  ...  robot path planning.  ...  Google Deep-Mind incorporated DQN into the Actor-Critic framework in 2015 and proposed the Deep Deterministic Policy Gradient (DDPG) to solve the problem of continuous action space.  ... 
doi:10.3390/s22093579 pmid:35591271 pmcid:PMC9102217 fatcat:t5rziagahndg3d6ib5xr5cxxeu

Mobile robots interacting with obstacles control based on artificial intelligence

Duc Chuyen Tran, Van Hoa Roan, Duc Dien Nguyen, Tung Lam Nguyen
2021 International Conference on Research in Intelligent and Computing in Engineering  
In this paper, research on the applications of artificial intelligence in implementing Deep Deterministic Policy Gradient (DDPG) on Gazebo model and the reality of mobile robot has been studied and applied  ...  When the robot moves in an environment with obstacles, the robot will automatically control to avoid these obstacles.  ...  N In this paper, the authors present a robot control problem based on an intelligent and modern Deep Deterministic Policy Gradient (DDPG).  ... 
doi:10.15439/2021r21 dblp:conf/rice/TranRN021 fatcat:mfncugjgfbeq5ndxcxdphsq3s4
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