What are major issues with Q-Learning?
A major limitation of Q-learning is that it is only works in environments with discrete and finite state and action spaces.
What is the target network in DQN?
An important element of DQN is a target network, a technique introduced to stabilize learning. A target network is a copy of the action-value function (or Q-function) that is held constant to serve as a stable target for learning for some fixed number of timesteps.
Does Q-Learning always converge?
In practice, a reinforcement learning algorithm is considered to converge when the learning curve gets flat and no longer increases. However, other elements should be taken into account since it depends on your use case and your setup. In theory, Q-Learning has been proven to converge towards the optimal solution.
What do you mean by not converging neural network weights?
What do you mean by not converging? – do you just mean that the performance (on training set) is bad, or that your weights are not converging… if weight convergence, then you need to use a lower learning rate, or a tapering learning rate. You might also want to use logistic/linear regression as a base line. lastly, how correlated are your inputs?
How is reinforcement learning done in deep Q networks?
The way it is done is by giving the Agent rewards or punishments based on the actions it has performed on different scenarios. One of the first practical Reinforcement Learning methods I learned was Deep Q Networks, and I believe it’s an excellent kickstart to this journey.
How to combine Q learning and deep learning?
We combine Q Learning and Deep Learning, which yields Deep Q Networks. The idea is simple: we’ll replace the the Q Learning’s table with a neural network that tries to approximate Q Values.
What’s the underlying principle of a deep Q Network?
The underlying principle of a Deep Q Network is very similar to the Q Learning algorithm. It starts with arbitrary Q-value estimates and explores the environment using the ε-greedy policy.