In order to extend the DQN-based reinforcement learning approach to handle more complex and diverse types of contexts, several strategies can be implemented:
Feature Engineering: Enhancing the feature representation of the contexts can allow the DQN model to capture more intricate details. This can involve using advanced embedding techniques or incorporating additional contextual information to enrich the input data.
Hierarchical Reinforcement Learning: Implementing a hierarchical RL approach can enable the model to learn at multiple levels of abstraction. This can help in handling complex contexts by breaking them down into simpler sub-contexts for more effective integration.
Transfer Learning: Leveraging transfer learning techniques can enable the model to generalize its learnings from one context to another. By pre-training the DQN on a diverse set of contexts, it can adapt more efficiently to new and complex scenarios.
Reward Design: Designing a more sophisticated reward function that considers multiple aspects of context integration, such as accuracy, coherence, and relevance, can guide the model towards optimal decision-making in diverse contexts.
Ensemble Learning: Combining multiple DQN models trained on different types of contexts can lead to a more robust and versatile system. Ensemble learning can help in handling the diversity of contexts by aggregating the strengths of individual models.
By incorporating these strategies, the DQN-based reinforcement learning approach can be extended to effectively handle a wide range of complex and diverse context types, beyond the condensed formats explored in the study.