Uncertainty-Based Advice for Deep Reinforcement Learning Agents
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Solution Overview
Problem
Reinforcement learning techniques face high sample-complexity challenges in practical applications, limiting their effectiveness in sequential decision-making problems.
Innovation Solution
A computer-implemented system and method that trains a learning agent using a reinforcement learning neural network, which calculates epistemic uncertainty and requests advice from a demonstrator when uncertainty exceeds a threshold, allowing the agent to implement suggested actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reinforcement learning techniques are used for sequential decision making, then learning capability is improved, but sample-complexity increases
Solution Approach 1:
The patent introduces an uncertainty estimation module as an intermediary between the reinforcement learning agent and the environment. This module calculates epistemic uncertainty metrics (such as entropy or variance of value function estimates) to determine when the agent should seek advice from a demonstrator rather than exploring independently, thereby reducing unnecessary samples while maintaining learning effectiveness
Solution Approach 2:
The system implements a feedback mechanism where the agent continuously monitors its own uncertainty levels and adjusts its behavior accordingly. When uncertainty exceeds a threshold, the agent requests advice from the demonstrator; when uncertainty is low, the agent explores independently. This closed-loop feedback reduces sample complexity by avoiding redundant advice requests and focusing learning resources on uncertain states
2Productivity
If the learning agent seeks advice frequently, then learning efficiency is improved, but resource usage increases
Solution Approach 1:
The learning agent performs self-assessment by calculating its own epistemic uncertainty metrics to determine when advice is needed. This self-service mechanism eliminates the need for continuous external monitoring and enables the agent to autonomously manage its learning process, requesting advice only when truly necessary and thereby reducing resource consumption
Solution Approach 2:
The system dynamically adjusts the agent's learning behavior based on changing uncertainty parameters. When epistemic uncertainty (measured through entropy or value function variance) exceeds a predefined threshold, the agent switches to advice-seeking mode; when uncertainty decreases, it returns to exploration mode. This parameter-driven adaptive behavior optimizes the balance between learning efficiency and resource usage
Data Source
AI summary
Disclosed are systems, methods, and devices for training a learning agent. A learning agent that maintains a reinforcement learning neural network is instantiated. State data reflective of a state of an environment explored by the learning agent is received. An uncertainty metric calculated upon processing the state data, the uncertainty metric measuring epistemic uncertainty of the learning agent. Upon determining that the uncertainty metric exceeds a pre-defined threshold: a request signal requesting an action suggestion from a demonstrator is sent; a suggestion signal reflective of the action suggestion is received; and an action signal to implement the action suggestion is sent.