What is the difference between fully observed and partially observed state features in Reinforcement learning ?
A fully observed state means that there is no hidden information. Clear examples of this are chess and Go because both players have all the information. The fact that both these games are deterministic doesn't matter. A game where the state changes are stochastic can still be fully observable. Games like poker, where both players can observe their own hand but not their opponents' are called partially observable. Other examples of this can be real time strategy games like Starcraft where you can only see in the line of sight of your units.