To avoid overfitting in reinforcement learning (RL) models, focus on techniques that encourage generalization and reduce dependency on specific training conditions. Overfitting in RL occurs when an agent performs well in its training environment but fails in new scenarios. Key strategies include diversifying training environments, applying regularization, and rigorously evaluating performance in unseen settings. These methods help ensure the agent learns adaptable policies rather than memorizing narrow solutions.
One effective approach is environment randomization, which exposes the agent to varied conditions during training. For example, in a robot navigation task, randomizing factors like floor friction, lighting, or obstacle layouts forces the agent to adapt to uncertainty. In autonomous driving simulations, varying weather, traffic patterns, or sensor noise prevents the model from over-relying on static conditions. Tools like domain randomization or procedural content generation (e.g., creating randomized game levels) systematically introduce diversity. This variability ensures the agent learns robust features instead of memorizing specific trajectories, improving generalization to real-world scenarios.
Another strategy involves regularization techniques common in supervised learning, adapted for RL. Adding dropout layers or L2 regularization to neural networks discourages over-reliance on specific neurons or weights. For policy-based methods like Proximal Policy Optimization (PPO), entropy regularization encourages exploration by penalizing overly deterministic policies. In value-based methods like DQN, adding noise to observations or actions (e.g., using epsilon-greedy exploration) can prevent the Q-network from fixating on narrow patterns. For example, in Deep Deterministic Policy Gradient (DDPG), adding Gaussian noise to actions during training helps the agent discover diverse strategies without destabilizing learning.
Finally, rigorous evaluation and early stopping are critical. Unlike supervised learning, RL lacks a clear train-test split, so validation requires separate environments unseen during training. For instance, an agent trained on 100 procedurally generated mazes should be tested on a new set of mazes to measure generalization. Early stopping—halting training when validation performance plateaus—prevents the agent from over-optimizing to the training environment. Tools like OpenAI Gym’s wrappers or custom evaluation pipelines can automate this process. Combining these methods with curriculum learning (gradually increasing task complexity) further refines the balance between exploration and exploitation, ensuring the agent learns transferable skills.