High MAE and Loss with good performance, but low MAE and Loss with worse performance?

I have a deep-q reinforcement learning model, and when i train it with neural network A, I get high scores - 2-3x better than random (score for random is avrg 0 per step, completing the task after 223000 steps, score for this is 1-2 per step, completing in more like 80000 steps). The reported Mean absolute error for this ranges in the 200-250 range, and the loss at something like 2000 - 2100. When i train with neural network B, i get MAEs of 45 and losses of 23, but the overall score is at 1.1x better than random, with 210000 steps needed to complete the task. What does this mean? Why is the network being better at predicting outcomes correlated inversely to results?