Beyond mutual information minimization, several techniques can be explored to further enhance the zero-shot transfer capabilities of RL agents:
Curriculum Learning: Implementing a curriculum learning strategy where the difficulty of the training levels gradually increases can help the agent build robust and transferable skills. By starting with simpler tasks and progressively introducing more complex challenges, the agent can learn a diverse set of skills that generalize well to new environments.
Meta-Learning: Utilizing meta-learning approaches to train the agent on a variety of tasks or environments can improve its ability to adapt quickly to new scenarios. Meta-learning algorithms can enable the agent to learn how to learn, leading to enhanced zero-shot transfer capabilities.
Self-Supervised Learning: Incorporating self-supervised learning techniques can help the agent learn useful representations or features from the environment without explicit supervision. By learning to predict certain aspects of the environment, the agent can develop a more comprehensive understanding that aids in zero-shot transfer.
Ensemble Methods: Employing ensemble methods to train multiple agents with diverse strategies or policies can improve the agent's robustness and generalization. By combining the predictions of multiple agents, the ensemble can make more informed decisions in novel environments.
Adversarial Training: Introducing adversarial training to expose the agent to challenging and adversarial scenarios during training can enhance its resilience and adaptability. Adversarial training can help the agent learn to handle unexpected situations and improve its zero-shot transfer capabilities.