Creating Hierarchical Dispositions of Needs in an Agent
This addresses the challenge of goal formation in reinforcement learning agents, though it appears incremental as it builds on existing hierarchical methods.
The paper tackles the problem of prioritizing competing objectives in agents by learning hierarchical abstractions, resulting in improved global expected rewards and state-of-the-art performance on the Pendulum v1 environment.
We present a novel method for learning hierarchical abstractions that prioritize competing objectives, leading to improved global expected rewards. Our approach employs a secondary rewarding agent with multiple scalar outputs, each associated with a distinct level of abstraction. The traditional agent then learns to maximize these outputs in a hierarchical manner, conditioning each level on the maximization of the preceding level. We derive an equation that orders these scalar values and the global reward by priority, inducing a hierarchy of needs that informs goal formation. Experimental results on the Pendulum v1 environment demonstrate superior performance compared to a baseline implementation.We achieved state of the art results.