Hierarchical Learning Agents for Complex Environment Decisions
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Solution Overview
Problem
Existing machine learning models struggle to develop a single, testable model of cognition for decision-making in complex environments, leading to inefficiencies in computational applications.
Innovation Solution
A biologically inspired agent architecture with a hierarchical design integrates self-attention, goal learning, working memory, and affective regulation, utilizing Reinforcement Learning (RL) with hyperparameter auto-tuning and Options discovery to enhance agent performance in navigating complex environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing machine learning models are used for decision-making in complex environments, then the system structure is relatively simple, but the decision-making performance and adaptability are insufficient
Solution Approach 1:
The agent architecture is segmented into multiple specialized modules: value computation module, policy module, attention module, working memory module, and affective regulation module. Each module handles specific cognitive functions, allowing the system to achieve complex decision-making capabilities through coordinated specialized components rather than a monolithic structure.
Solution Approach 2:
The patent implements a hierarchical nested architecture where the attention mechanism is nested within the value computation module, working memory is nested within the policy module, and affective regulation is nested at the highest level coordinating all lower-level modules. This nested structure enables compact organization of complex functions while maintaining modularity.
2Adaptability or versatility
If a biologically inspired hierarchical agent architecture is implemented, then decision-making capabilities and adaptability are improved, but computational complexity and resource requirements increase
Solution Approach 1:
The attention mechanism pre-processes and prioritizes state features before they are fed into the value computation module. Working memory pre-organizes action sequences before policy execution. This preliminary processing reduces the computational burden on downstream modules by filtering and organizing information in advance.
Solution Approach 2:
The affective regulation module dynamically adjusts the agent's behavior by modulating the output of the policy module based on computed emotional states. The system transitions between different behavioral modes (exploration vs. exploitation, risk-averse vs. risk-seeking) dynamically, allowing adaptive computational resource allocation based on environmental conditions.
3Reliability
If reinforcement learning with hyperparameter auto-tuning is used, then agent performance in non-stationary environments is improved, but training time and computational resources increase
Solution Approach 1:
The hyperparameter auto-tuning mechanism continuously monitors agent performance in the non-stationary environment and automatically adjusts learning rates, exploration parameters, and other hyperparameters based on performance feedback. This closed-loop control enables the system to adapt to changing environmental conditions without manual intervention, maintaining high performance despite increased computational overhead.
Data Source
AI summary
Embodiments disclosed include a method comprising building a machine learning (ML) model that includes a digital agent configured to navigate a digital environment, implementing a navigation of the digital agent over a period of time and following a path within the digital environment, that includes a set of states. Upon reaching each state, the digital agent is configured to acquire the value associated with that state. The method includes determining a signal representing an acquisition of values by the digital agent over the period of time, computing an emotive state of the digital agent and an arousal state of the digital agent based on the signal representing the acquisition of values, and determining a goal for the digital agent to achieve an improved overall performance in acquiring cumulative rewards. The method includes implementing a real version of an action of the digital agent in a real environment.