np zeros((env observation_space nenv action_space )for episode in range( num_episodes )inside the while trueinner game loopcreate noise noiseor meaninglessrandom datais sometimes introduced when training deep neural networks because it can improve both the performance and the accuracy of the model note that the higher the noisethe less the values in [state:matter as resultthe higher the noisethe more likely that the agent acts independently of its knowledge of the game in other wordshigher noise encourages the agent to explore random actions/ataribot/bot_ _q_table py while truenoise np random random(( env action_space )(episode** action env action_space sample(note that as episodes increasesthe amount of noise decreases quadraticallyas time goes onthe agent explores less and less because it can trust its own assessment of the game' reward and begin to exploit its knowledge update the action line to have your agent pick actions according to the -value tablewith some exploration built in