What is the role of exploration in the early stages of reinforcement learning?

In reinforcement learning (RL), exploration in the early stages is critical for the agent to discover useful actions and build a foundation for effective decision-making. At the start of training, the agent has no prior knowledge of the environment’s dynamics or reward structure. Without exploration, the agent might prematurely settle on suboptimal actions, missing better strategies. For example, a robot learning to navigate a maze might initially turn right at every intersection due to a small early reward but fail to discover a shorter path to the left. Exploration ensures the agent tests diverse actions to gather data, avoiding overcommitment to early—and potentially flawed—patterns.

Exploration strategies like epsilon-greedy, Thompson sampling, or curiosity-driven methods are commonly used to balance trying new actions versus exploiting known rewards. For instance, epsilon-greedy forces the agent to take random actions (e.g., 10% of the time) to sample the environment, even if it already has a preferred action. Similarly, Thompson sampling uses probabilistic models to prioritize actions with uncertain outcomes, encouraging the agent to resolve ambiguity. In a grid-world task, an agent might initially wander to map obstacles or locate high-reward zones, which would be impossible if it only followed a greedy policy. These methods ensure the agent builds a robust understanding of the environment before refining its strategy.

As training progresses, exploration typically decreases in favor of exploitation, but early emphasis on exploration sets the stage for long-term success. For example, in complex environments like video games, an agent that doesn’t explore enough early on might never discover critical items or mechanics required to progress. A lack of initial exploration can also lead to catastrophic forgetting—where the agent’s policy becomes too rigid to adapt to new scenarios. Developers often tune exploration parameters (like epsilon decay rates) to match the environment’s complexity: sparse or deceptive rewards demand more exploration. Without this early phase, the agent’s policy risks being myopic, making exploration a foundational step in RL workflows.