Abstract
Abstract—In this study, we perform a comparative analysis of two approaches we developed for learning to carryout pick and place operations on various objects moving on a conveyor belt in a non-visual environment, using proximity sensors. The problem under consideration is formulated as a Markov Decision Process. and solved by using Reinforcement Learning algorithms. Learning robotic manipulations using simple reward signals is still considered to be an unresolved problem. Our reinforcement learning algorithms are based on model-free off-policy training using Q-Learning and on-policy training using SARSA. Training and testing of both algorithms along with detailed a comparison analysis are performed in a simulation-based testbed.
Original language | English |
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Pages (from-to) | 526-535 |
Number of pages | 10 |
Journal | International Journal of Mechanical Engineering and Robotics Research |
Volume | 10 |
Issue number | 10 |
DOIs | |
Publication status | Published - 2021 |
Keywords
- Markov decision problem
- Q-learning
- SARSA
- non-visual
- reinforcement learning
- robotic manipulation