Intro to reinforcement learning
Q - learning in real time
Greedy epsilon
state action pairs in anticipation of a reward
Updates the estimate of Q and not the memory
Neural networks - 2 networks for reducing biases
Q (evaluation) - what action to take
Q (target) - value of maximal actions during the learning step
Cartpole -
Reinforcement Learning in 3 Hours | Full Course using Python