Overfitting in reinforcement learning (RL) occurs when an agent learns to perform well in its training environment but fails to generalize to new, unseen environments or scenarios. This happens because the agent optimizes its policy (the strategy it uses to make decisions) too closely to the specific details of the training setup, such as particular environmental dynamics, reward structures, or initial conditions. Instead of learning broadly applicable rules, the agent becomes overly specialized, making it brittle when faced with variations or unpredictability in real-world settings.
A common example of overfitting in RL is an agent trained in a simulated environment with fixed parameters. For instance, imagine training a robot to navigate a maze where the walls are always placed in the same locations. The agent might memorize a precise sequence of turns to reach the goal but struggle if the maze layout changes even slightly. Similarly, in a game-playing scenario, an agent might exploit quirks in the training environment—like predictable opponent behavior or deterministic physics—to maximize rewards, but these strategies would fail against more adaptive opponents or in environments with randomized elements. Overfitting often arises when the training data lacks diversity, or the agent’s exploration is too limited to encounter a wide range of situations.
To mitigate overfitting, developers can use techniques like domain randomization, where environmental parameters (e.g., lighting, friction, object positions) are varied during training to expose the agent to a broader set of conditions. Regularization methods, such as adding noise to the agent’s observations or actions, can also encourage robustness. Another approach is to evaluate the agent in validation environments that are distinct from the training setup, ensuring it doesn’t over-optimize for the training context. For example, training a self-driving car simulator with varying weather conditions and traffic patterns—rather than a single scenario—helps the agent adapt to real-world unpredictability. By prioritizing generalization during training, developers can build RL systems that perform reliably in diverse, dynamic settings.