statethe state at current time step actionthe action taken at current time step rewardthe reward for current time step state'the new state for next time stepgiven that we took action action'all possible actions learning_ratethe learning rate discount_factorthe discount factorhow much reward "degradesas we propagate it for complete explanation of these two equationssee this article on understanding -learning with this understanding of reinforcement learning in mindall that remains is to actually run the game and obtain these -value estimates for new policy step -creating simple -learning agent for frozen lake now that you have baseline agentyou can begin creating new agents and compare them against the original in this stepyou will create an agent that uses -learninga reinforcement learning technique used to teach an agent which action to take given certain state this agent will play new gamefrozenlake the setup for this game is described as follows on the gym websitewinter is here you and your friends were tossing around frisbee at the park when you made wild throw that left the frisbee out in the middle of the lake the water is mostly frozenbut there are few holes where the ice has melted if you step into one of those holesyou'll fall into the freezing