Curiosity-driven exploration methods are techniques in reinforcement learning (RL) that encourage agents to explore their environment by rewarding behaviors that lead to novel or uncertain states. Unlike traditional RL, where agents optimize for external rewards (e.g., points in a game), these methods use intrinsic rewards—self-generated signals based on the agent’s own learning progress. The goal is to drive the agent to explore areas of the environment it hasn’t yet mastered, which helps overcome challenges like sparse rewards or deceptive local optima. For example, in a maze-solving task, an agent might receive no external reward until it finds the exit, but curiosity-driven methods would motivate it to explore new paths, accelerating discovery.
A common approach involves prediction-based curiosity. Here, the agent learns a model to predict the outcome of its actions, and the intrinsic reward is tied to how wrong those predictions are. For instance, the Intrinsic Curiosity Module (ICM) uses two neural networks: one predicts the next state given the current state and action, and another learns a compressed representation of the state to focus on relevant features. The agent receives higher rewards when its predictions fail, indicating unfamiliar states. Another method, Random Network Distillation (RND), uses two networks: a fixed random network and a trainable one that tries to mimic its outputs. The prediction error between them serves as a curiosity signal, encouraging exploration of states where the error is high.
These methods are particularly useful in environments with sparse or delayed rewards. For example, a robot learning to walk might only receive a reward when it moves forward, but curiosity-driven exploration could help it discover intermediate behaviors like shifting weight or balancing. However, challenges remain. Prediction-based methods can be distracted by “noisy” or stochastic environments where states are inherently unpredictable, leading to wasted exploration. To address this, some approaches filter out uncontrollable aspects of the environment (e.g., background animations in a game) by focusing on state features the agent can influence. While not a universal solution, curiosity-driven methods significantly improve exploration efficiency in complex, open-ended tasks, making them a key tool for developers training RL agents in real-world scenarios.