for in range(num_episodes)episode_reward states [downsample(env reset())while trueinstead of accepting one state at timethe new neural network accepts four states at time as resultyou must wait until the list of states contains at least four states before applying the pretrained model add the following lines below the line reading while truethese tell the agent to take random action if there are fewer than four states or to concatenate the states and pass it to the pretrained model if there are at least four/ataribot/bot_ _dqn py while trueif len(states action env action_space sample(elseframes np concatenate(states[- :]axis= action np argmax(model([frames])then take an action and update the relevant data add downsampled version of the observed stateand update the reward for this episode