if len(states> stateslabels [][state one_hot(env reset()n_obsepisode_reward while truestates append(statenoise np random random(( n_actions)episode action np argmax( (statenoisestate rewarddone_ env step(actionstate one_hot(state n_obsqtarget reward discount_factor np max( (state )label (statelabel[action( learning_ratelabel[actionlearning_rate qtarget labels append(labelepisode_reward +reward state state if len(states = wq train(np array(states)np array(labels)wif donerewards append(episode_rewardif episode report_interval = print_report(rewardsepisodebreak print_report(rewards-