np zeros((env observation_space nenv action_space )for episode in range( num_episodes )state env reset(episode_reward while truenoise np random random(( env action_space )(episode** action np argmax( [state:noisestate rewarddone_ env step(actionqtarget reward discount_factor np max( [state :] [stateaction -learning_rate [stateactionlearning_rate qtarget episode_reward +reward state state if donerewards append(episode_rewardbreak our logic for training the agent is now complete all that' left is to add reporting mechanisms even though python does not enforce strict type checkingadd types to your function declarations for cleanliness at the top of the filebefore the first line reading import gymimport the list type