Hierarchical RL Controller Using Natural Language Subgoals
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Solution Overview
Problem
Traditional reinforcement learning methods struggle to control agents in real-world settings, particularly in tasks requiring goal-directed behavior over long time horizons, due to challenges in exploration and temporal credit assignment.
Innovation Solution
The system employs a hierarchical reinforcement learning approach using a high-level controller neural network to generate natural language commands, which are then processed by a low-level controller neural network to produce control inputs for the agent.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of moving object
If traditional reinforcement learning methods are used to control agents in real-world settings, then the agent can learn from exploration, but the agent struggles with goal-directed behavior over long time horizons due to challenges in temporal credit assignment
Solution Approach 1:
The patent divides the control system into hierarchical levels: a high-level controller that generates natural language commands specifying subgoals, and a low-level controller that executes specific actions. This segmentation allows the agent to break down long-horizon tasks into manageable sub-tasks, improving temporal credit assignment and goal-directed behavior success rates
2Device complexity
If a single controller neural network is used to directly map observations to control outputs, then the system structure is simple, but the system cannot effectively perform complex tasks requiring long sequences of motor actions
Solution Approach 1:
The controller is segmented into multiple specialized networks: a high-level controller neural network that generates natural language commands, and a low-level controller neural network that maps commands to control outputs. This segmentation enables the system to handle complex tasks while maintaining manageable network architectures through specialized function division
Solution Approach 2:
Natural language commands serve as an intermediary representation between high-level intent and low-level execution. The high-level controller generates these commands which then guide the low-level controller, enabling effective communication across hierarchical levels and improving task performance
3Productivity
If reinforcement learning with exploration is used, then the agent can discover behaviors, but the data-hungry process and temporal credit assignment requirements make it impractical for real-world embodied tasks
Solution Approach 1:
The patent employs supervised learning with demonstration trajectories as training data, allowing the controller to learn from pre-collected examples rather than requiring extensive exploration from scratch. This preliminary action approach significantly reduces training time and data requirements while maintaining learning effectiveness
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a high-level controller neural network for controlling an agent. In particular, the high-level controller neural network generates natural language commands that can be provided as input to a low-level controller neural network, which generates control outputs that can be used to control the agent.