Planning with Arithmetic and Geometric Attributes
This work addresses the challenge of sample efficiency in reinforcement learning for agents dealing with structured environments, though it appears incremental as it builds on existing attribute-augmentation methods.
The paper tackled the problem of enabling intelligent agents to generalize faster to novel tasks by exploiting geometric and arithmetic structures in the environment, resulting in substantial gains in sample complexity.
A desirable property of an intelligent agent is its ability to understand its environment to quickly generalize to novel tasks and compose simpler tasks into more complex ones. If the environment has geometric or arithmetic structure, the agent should exploit these for faster generalization. Building on recent work that augments the environment with user-specified attributes, we show that further equipping these attributes with the appropriate geometric and arithmetic structure brings substantial gains in sample complexity.