To extend DRED to handle more complex environments with higher-dimensional parameter spaces, several modifications and enhancements can be considered:
Advanced Generative Models: Instead of using a VAE, more sophisticated generative models like GANs or flow-based models could be employed to better approximate the complex distribution of level parameters. These models can capture intricate relationships and dependencies in the data, leading to more diverse and realistic generated levels.
Hierarchical Sampling Strategies: Introducing a hierarchical sampling strategy where levels are sampled at different abstraction levels could help in handling the higher-dimensional parameter spaces. This approach can provide a structured way to generate levels with varying complexities and features.
Adaptive Level Generation: Implementing adaptive level generation techniques that adjust the generation process based on the agent's learning progress and performance could enhance the diversity and challenge of the generated levels. This adaptability can ensure that the agent is continually exposed to new and challenging environments.
Transfer Learning: Leveraging transfer learning techniques to transfer knowledge and insights gained from simpler environments to more complex ones can expedite the learning process in higher-dimensional spaces. By transferring learned representations or policies, the agent can adapt more efficiently to novel environments.
Dynamic Environment Design: Implementing a dynamic environment design approach where the environment parameters evolve or change over time can introduce additional complexity and variability. This dynamic nature can keep the agent adaptable and responsive to changing conditions in the environment.