Overfitting in reinforcement learning refers to the situation where an agent learns a policy that performs well on the training environment but poorly on new, unseen scenarios or environments. This occurs when the model becomes too specialized to the specific experiences it encounters during training, failing to generalize.
Overfitting can be particularly problematic in environments with stochastic dynamics or those that are highly variable. For example, an agent that only learns to perform well in one particular game level might struggle to adapt to new levels with different conditions.
To prevent overfitting, regularization techniques, such as dropout or experience replay with diverse samples, are often employed. Additionally, using more exploration during training and avoiding overreliance on a fixed training set can help improve generalization and prevent the agent from overfitting to specific conditions.