Reinforcement learning (RL) is a machine learning technique with strong links to natural learning. However, it shares several "unnatural" limitations with many other successful machine learning algorithms. RL agents are not typically able to take advice or to adjust to new situations beyond the specific problem they are asked to learn. Due to limitations like these, RL remains slower and less adaptable than natural learning. Our recent work focuses on extending RL to include the naturally inspired abilities of advice taking and transfer learning. Through experiments in the RoboCup domain, we show that doing so can make RL faster and more adaptable.