I want to build a reinforcement learning model, which takes a camera picture as input, that learns online (in terms of machine learning). Based on the position of an object on the camera, I want the model to output an action. That action would be a stepper motor, that either moves to the right or left. This process would be repeated until a given goal/position is reached.
I can't go to the lab at the moment, so I wrote a virtual environment and let the agent live in that.
I am trying a neural network with the cross-entropy function. For small environments, this works fine. However, when I increase the size of the environment, the computation becomes really really slow and the model needs a lot of data input until it starts to learn. Also, it only learns offline. But what I would rather want is a model that learns online and only takes a few tries until it understands the underlying pattern. That isn't a problem for the virtual environment, since I can easily get thousands of data samples. But in the real environment, it would take ages this way.
- Is there an online reinforcement algorithm that could help me out (instead of training the neural network with the cross-entropy loss function)?