The concept of compressed environments can be further extended or generalized in MARL to handle more complex information structures in the following ways:
Hierarchical Compression: Instead of compressing the entire environment into a single compressed version, hierarchical compression can be used to create multiple levels of compressed environments. This hierarchical approach can help in handling complex information structures by capturing different levels of abstraction in the environment.
Dynamic Compression: Introducing dynamic compression techniques where the level of compression can adapt based on the complexity of the environment or the learning progress of the agents. This dynamic approach can help in efficiently handling varying levels of information complexity.
Adaptive Distortion Measures: Developing adaptive distortion measures that can capture the specific characteristics of the environment and the learning task. By tailoring the distortion measure to the specific requirements of the task, the compressed environments can better represent the essential information for decision-making.
Multi-Resolution Compression: Implementing multi-resolution compression techniques where different parts of the environment are compressed at varying levels of detail. This approach can help in efficiently representing complex information structures while maintaining the essential details for decision-making.
By exploring these extensions and generalizations, the concept of compressed environments in MARL can be further enhanced to handle more intricate information structures effectively.