Sample efficiency in reinforcement learning (RL) refers to how well an algorithm learns a task relative to the number of interactions (or “samples”) it requires with the environment. A sample-efficient algorithm achieves good performance with minimal data, while an inefficient one might need millions of trials to reach the same result. This matters because real-world environments—like robotics or industrial systems—often have high costs or risks associated with data collection. For example, training a physical robot through trial and error is time-consuming and could damage hardware. Sample efficiency determines whether an RL solution is practical for such scenarios.
Improving sample efficiency often involves techniques that maximize the usefulness of each interaction. One common approach is experience replay, used in algorithms like DQN, which stores past transitions in a buffer and reuses them for training. This helps the agent learn from rare or critical events multiple times. Another method is model-based RL, where the agent builds a predictive model of the environment to simulate outcomes without real interactions. For instance, AlphaGo reduced real-game training by first learning from human games and then refining its strategy through self-play simulations. Additionally, off-policy learning allows agents to learn from data generated by older policies or even human demonstrations, as seen in applications like autonomous driving, where historical driving data accelerates training.
However, improving sample efficiency involves trade-offs. Model-based methods rely on accurate environment models, which can be difficult to create for complex systems like weather prediction. Experience replay buffers require careful tuning to avoid overfitting to outdated data. Exploration strategies like curiosity-driven learning can help agents discover useful states faster, but they may also lead to irrelevant or risky behaviors. For example, a robot programmed to explore novel states might prioritize moving its arm randomly instead of completing a task. Ultimately, the right balance depends on the problem: simulations might suffice for video games, but real-world tasks demand algorithms that minimize costly trial and error. Developers must weigh these factors when choosing or designing RL solutions.