To prevent overfitting in reinforcement learning (RL) models, several strategies can be employed.
Regularization techniques: Like in supervised learning, applying regularization methods such as dropout or L2 regularization can help the model generalize better and avoid overfitting to specific experiences. This prevents the model from becoming too reliant on particular state-action pairs.
Experience replay: In methods like Q-learning, experience replay stores past experiences and samples from this pool to train the agent, ensuring that the model does not become overfitted to recent experiences. This improves the agent's ability to generalize over time.
Exploration: Ensuring sufficient exploration during training, such as using epsilon-greedy policies or other exploration strategies, prevents the agent from becoming too focused on certain actions or states and encourages it to discover new strategies.
Training on diverse environments: Exposing the agent to varied conditions or environments helps it learn policies that are more robust and generalizable. This reduces the risk of overfitting to a single environment.