reinforcement learning representing agents and environments when the learning agent does an action in the environmentit observes (staterewardpair from the environment the state is the world statethis is the fully observable assumption an rl environment implements do(actionmethod that returns (staterewardpair rlproblem py -representations for reinforcement learning import random from display import displayable from utilities import flip class rl_env(displayable)def __init__(self,actions,state)self actions actions set of actions self state state initial state def do(selfaction)"""do action returns state,reward ""raise notimplementederror("rl_env do"abstract method here is the definition of the simple -state -action party/relax decision rlproblem py -(continued class healthy_env(rl_env)def __init__(self)rl_env __init__(self,["party","relax"]"healthy"