In order to enhance the attention mechanism in TEMP to better capture the dynamic relationships between environmental information, task objectives, and historical planning data, several strategies could be implemented:
Multi-Head Attention Refinement: By increasing the number of attention heads in the Multi-Head Attention mechanism, TEMP can capture more intricate relationships between different data sources. Each attention head can focus on specific aspects of the input data, allowing for a more nuanced understanding of the environment and task objectives.
Temporal Attention: Introducing a temporal attention mechanism could enable TEMP to consider the temporal dynamics of the planning process. By incorporating information about the sequence of actions taken during planning, TEMP can adapt its focus based on the historical planning data, leading to more informed decision-making.
Adaptive Attention Weights: Implementing adaptive attention weights that dynamically adjust based on the relevance of different data sources at each planning step can improve the model's ability to prioritize information. By learning to assign varying levels of importance to environmental information, task objectives, and historical planning data, TEMP can optimize its decision-making process.
Hierarchical Attention: Utilizing a hierarchical attention mechanism can allow TEMP to capture relationships at different levels of abstraction. By hierarchically attending to different granularities of data, TEMP can extract meaningful patterns and dependencies, leading to more effective planning strategies.