q_target tf matmul(obs_tp _phwq_target_max tf reduce_max(q_target_phaxis= q_target_sa rew_ph discount_factor q_target_max q_current_sa q_current[ act_pherror tf reduce_sum(tf square(q_target_sa q_current_sa)pred_act_ph tf argmax(q_current np zeros((env observation_space nenv action_space )for episode in range( num_episodes )after setting up your algorithm and the loss functiondefine your optimizer/ataribot/bot_ _q_network py error tf reduce_sum(tf square(q_target_sa q_current_sa)pred_act_ph tf argmax(q_current setup optimization trainer tf train gradientdescentoptimizer(learning_rate=learning_rateupdate_model trainer minimize(errorq np zeros((env observation_space nenv action_space )for episode in range( num_episodes )