Finding Meaning in Sparse Rewards
Some problems in life are inherently sparse. You can work hard, make many decisions, and still receive little or no feedback. Imagine working on a large research project you believe in, without any external guidance. You keep making progress, but the environment provides no clear signals about whether you are on the right track.
In reinforcement learning, such “sparse reward” situations are often addressed by distinguishing between extrinsic and intrinsic rewards. Extrinsic rewards come from the environment, for example achieving a goal or receiving explicit approval. Intrinsic rewards are self-generated, and they reflect your own sense of progress or curiosity, even when no external signal is present.
This is powerful because, even without external feedback, you can still evaluate your own progress and keep moving forward. Your assessment may not be perfect, but it can lead you to explore previously unknown areas that might eventually yield high extrinsic rewards. Once you find such rewards, you can update your own evaluations, allowing value to propagate along the paths that lead there. Over time, you may shift from exploration toward exploitation, focusing on the strategies that work best.
This dynamic mirrors many real-life challenges. Often, the path to the biggest external rewards begins by trusting your own internal compass.
Some exploration methods, such as Model-Based Interval Estimation (MBIE) or Upper Confidence Bounds (UCB), aim to ensure that we make only a limited number of poor decisions before finding a good strategy. This principle is known as PAC-MDP. In simple terms, it is a mathematical way of showing that we can explore efficiently without wasting too many opportunities. In real life, some decisions are costly, so random trial and error can be risky. Instead, these methods encourage you to be optimistic in uncertain situations, that perspective will guide you to better choices and focus your exploration where it matters most.
Although these concepts are closely tied to building AI agents, they can be very useful in life. The main takeaways are:
- Sparse problems are common in real life, and they may hide very high rewards.
- To understand which things are good or bad, you must explore, but wisely. Approach uncertainty with optimism, instead of doing things randomly.
- To live a good life, explore wisely and then exploit.