Analysis of Q-learning on ANNs for robot control using live video feed
| Autor: | Nihal Murali, Surekha Bhanot, Kunal Gupta |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
0209 industrial biotechnology
Artificial neural network business.industry Computer science 020208 electrical & electronic engineering Q-learning Robotics 02 engineering and technology Robot learning Robot control Computer Science::Robotics 020901 industrial engineering & automation Control theory 0202 electrical engineering electronic engineering information engineering Robot Reinforcement learning Artificial intelligence business |
| Zdroj: | ICSIPA |
| Popis: | Training of artificial neural networks (ANNs) using reinforcement learning (RL) techniques is being widely discussed in the robot learning literature. The high model complexity of ANNs along with the model-free nature of RL algorithms provides a desirable combination for many robotics applications. There is a huge need for algorithms that generalize using raw sensory inputs, such as vision, without any hand-engineered features or domain heuristics. In this paper, the standard control problem of line following robot was used as a test-bed, and an ANN controller for the robot was trained on images from a live video feed using Q-learning. A virtual agent was first trained in simulation environment and then deployed onto a robot's hardware. The robot successfully learns to traverse a wide range of curves and displays excellent generalization ability. Qualitative analysis of the evolution of policies, performance and weights of the network provide insights into the nature and convergence of the learning algorithm. |
| Databáze: | OpenAIRE |
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