Reinforcement Training of Robot
Reinforcement learning is an important type of Machine Learning where an agent learn how to behave in a environment by performing actions and seeing the results.
In recent years, we’ve seen a lot of improvements in this fascinating area of research. Examples include DeepMind and the Deep Q learning architecture in 2014, beating the champion of the game of Go with AlphaGo in 2016, OpenAI and the PPO in 2017, amongst others.
Reinforcement Learning is summarised as follows
Points to remember in Reinforcement Learning
- Input: The input should be an initial state from which the model will start
- Output: There are many possible output as there are variety of solution to a particular problem
- Training: The training is based upon the input, The model will return a state and the user will decide to reward or punish the model based on its output.
- The model keeps continues to learn.
- The best solution is decided based on the maximum reward.
Following URL guide you to the Reinforcement Learning in brief (Q Learning & Deep Q Learning)
Above code trains the line follower robot to follow line made for robot. It used Q Learning approach to learn by itself. In this approach our bot update its Q table entry for each state it encounters by hit and trial approach and once all the entries in Q table are updated successfully it ready to follow its path.