A reinforcement learning with adaptive state space recruitment strategy for real autonomous mobile robots

2003 ◽  
Author(s):  
T. Kondo ◽  
K. Ito
Keyword(s):  
Reinforcement Learning ◽  
Mobile Robots ◽  
State Space ◽  
Autonomous Mobile Robots ◽  
Recruitment Strategy

State-chain sequential feedback reinforcement learning for path planning of autonomous mobile robots

2013 ◽  
Vol 14 (3) ◽  
pp. 167-178 ◽  
Author(s):  
Xin Ma ◽  
Ya Xu ◽  
Guo-qiang Sun ◽  
Li-xia Deng ◽  
Yi-bin Li
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Mobile Robots ◽  
Autonomous Mobile Robots

scholarly journals Iterative SARSA: The Modified SARSA Algorithm for Finding the Optimal Path

2020 ◽  
Vol 8 (6) ◽  
pp. 4333-4338
Keyword(s):  
Reinforcement Learning ◽  
Comparative Analysis ◽  
Mobile Robots ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Optimal Path ◽  
Autonomous Mobile Robots ◽  
Current Standard ◽  
Q Learning ◽  
Better Than

This paper presents a thorough comparative analysis of various reinforcement learning algorithms used by autonomous mobile robots for optimal path finding and, we propose a new algorithm called Iterative SARSA for the same. The main objective of the paper is to differentiate between the Q-learning and SARSA, and modify the latter. These algorithms use either the on-policy or off-policy methods of reinforcement learning. For the on-policy method, we have used the SARSA algorithm and for the off-policy method, the Q-learning algorithm has been used. These algorithms also have an impacting effect on finding the shortest path possible for the robot. Based on the results obtained, we have concluded how our algorithm is better than the current standard reinforcement learning algorithms

Self-Partitioning State Space for Behavior Acquisition of Vision-Based Mobile Robots

2001 ◽  
Vol 13 (6) ◽  
pp. 625-636 ◽  
Author(s):  
Takayuki Nakamura ◽  
◽  
Tsukasa Ogasawara
Keyword(s):  
Computer Simulation ◽  
Reinforcement Learning ◽  
Mobile Robots ◽  
State Space ◽  
Experimental Results ◽  
Generalization Capability ◽  
Soccer Robot ◽  
Generalization Problem ◽  
Real Robot ◽  
Robot Tasks

An input generalization problem is one of the most important in applying reinforcement learning to real robot tasks. To cope with this problem, we propose a self-partitioning state space algorithm, which can make nonuniform quantization of state space. To show that our algorithm has generalization capability, we apply our method to two tasks in which a soccer robot shoots a ball into a goal and prevent a ball from entering a goal. To show the validity of this method, the experimental results for computer simulation and a real robot are shown.

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