scholarly journals A Continuous-State Reinforcement Learning Strategy for Link Adaptation in OFDM Wireless Systems

2015 ◽  
Vol 30 (1) ◽  
pp. 47-57 ◽  
Author(s):  
P.H.P. Carvalho ◽  
R.D. Vieira ◽  
J.P. Leite
Keyword(s):  
Reinforcement Learning ◽  
Learning Strategy ◽  
Wireless Systems ◽  
Link Adaptation ◽  
Continuous State

scholarly journals Collision-free path planning for welding manipulator via hybrid algorithm of deep reinforcement learning and inverse kinematics

2021 ◽  
Author(s):  
Jie Zhong ◽  
Tao Wang ◽  
Lianglun Cheng
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Free Path ◽  
Inverse Kinematics ◽  
Multiple Dimensions ◽  
Continuous State ◽  
Planning Algorithm ◽  
Convergence Performance ◽  
Path Planner ◽  
Action Spaces

AbstractIn actual welding scenarios, an effective path planner is needed to find a collision-free path in the configuration space for the welding manipulator with obstacles around. However, as a state-of-the-art method, the sampling-based planner only satisfies the probability completeness and its computational complexity is sensitive with state dimension. In this paper, we propose a path planner for welding manipulators based on deep reinforcement learning for solving path planning problems in high-dimensional continuous state and action spaces. Compared with the sampling-based method, it is more robust and is less sensitive with state dimension. In detail, to improve the learning efficiency, we introduce the inverse kinematics module to provide prior knowledge while a gain module is also designed to avoid the local optimal policy, we integrate them into the training algorithm. To evaluate our proposed planning algorithm in multiple dimensions, we conducted multiple sets of path planning experiments for welding manipulators. The results show that our method not only improves the convergence performance but also is superior in terms of optimality and robustness of planning compared with most other planning algorithms.

scholarly journals Reinforcement learning versus swarm intelligence for autonomous multi-HAPS coordination

2021 ◽  
Vol 3 (6) ◽  
Author(s):  
Ogbonnaya Anicho ◽  
Philip B. Charlesworth ◽  
Gurvinder S. Baicher ◽  
Atulya K. Nagar
Keyword(s):  
Reinforcement Learning ◽  
State Space ◽  
Swarm Intelligence ◽  
Performance Indicators ◽  
Convergence Rates ◽  
Tuning Parameters ◽  
Continuous State Space ◽  
Continuous State ◽  
User Coverage ◽  
Better Than

AbstractThis work analyses the performance of Reinforcement Learning (RL) versus Swarm Intelligence (SI) for coordinating multiple unmanned High Altitude Platform Stations (HAPS) for communications area coverage. It builds upon previous work which looked at various elements of both algorithms. The main aim of this paper is to address the continuous state-space challenge within this work by using partitioning to manage the high dimensionality problem. This enabled comparing the performance of the classical cases of both RL and SI establishing a baseline for future comparisons of improved versions. From previous work, SI was observed to perform better across various key performance indicators. However, after tuning parameters and empirically choosing suitable partitioning ratio for the RL state space, it was observed that the SI algorithm still maintained superior coordination capability by achieving higher mean overall user coverage (about 20% better than the RL algorithm), in addition to faster convergence rates. Though the RL technique showed better average peak user coverage, the unpredictable coverage dip was a key weakness, making SI a more suitable algorithm within the context of this work.

Reinforcement Learning Based Link Adaptation in 5G URLLC

2021 ◽  
Author(s):  
Praveen S ◽  
Jihas Khan ◽  
Lillykutty Jacob
Keyword(s):  
Reinforcement Learning ◽  
Link Adaptation

A New Hybrid Deep Neural Architectural Search based Ensemble Reinforcement Learning Strategy for Wind Power Forecasting

2021 ◽  
pp. 1-1
Author(s):  
Seyed Mohammad Jafar Jalali ◽  
Gerardo J. Osorio ◽  
Sajad Ahmadian ◽  
Mohamed Lotfi ◽  
Vasco Campos ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Wind Power ◽  
Learning Strategy ◽  
Wind Power Forecasting ◽  
Power Forecasting
Sign in / Sign up

Export Citation Format

Share Document