Reinforcement Learning Action Selection With Variance-Gated Human Input
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Solution Overview
Problem
Current reinforcement learning policies face challenges in optimally interacting with complex environments, particularly when the variance in state transitions exceeds a threshold, leading to potential errors in action selection that could result in safety issues for autonomous vehicles and robots.
Innovation Solution
Implementing a system where a reinforcement learning agent determines the variance of environmental states and requests human intervention when the variance meets a threshold, while autonomously selecting actions based on a reinforcement learning policy when the variance does not meet the threshold, thereby ensuring safe and optimal decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If reinforcement learning agent autonomously selects actions based on learned policy, then productivity and speed of decision-making are improved, but reliability and safety deteriorate when state variance exceeds threshold
Solution Approach 1:
A variance threshold mechanism serves as an intermediary between the autonomous reinforcement learning agent and the environment. When state variance exceeds the threshold, the system pauses autonomous decision-making and requests human input, preventing potentially unsafe actions while maintaining autonomous operation during normal conditions. This mediator resolves the contradiction by enabling fast autonomous decisions when safe and human oversight when reliability is at risk.
Solution Approach 2:
The system dynamically adjusts its level of autonomy based on real-time variance assessment. When environmental state variance is low, the agent operates autonomously for high productivity. When variance exceeds the threshold, the system transitions to human-in-the-loop mode for improved reliability. This dynamic adjustment allows the system to optimize the trade-off between speed and safety depending on current conditions.
2Reliability
If human intervention is requested for all high-variance states, then reliability is improved, but loss of time increases due to human input delays
Solution Approach 1:
The variance threshold acts as a critical parameter that determines when human intervention is triggered. By carefully setting this threshold, the system ensures human input is requested only when truly necessary for safety, minimizing time loss while maintaining reliability. The threshold parameter balances the trade-off by filtering out routine high-variance situations that don't require human oversight.
Solution Approach 2:
Human intervention is applied selectively to specific high-risk states rather than universally to all high-variance situations. The system assesses local state characteristics and requests human input only when the specific state combination presents genuine safety concerns. This localized application of human oversight reduces unnecessary delays while maintaining reliability where needed.
3Manufacturing precision
If reinforcement learning operates in deterministic environments, then manufacturing precision of action selection is improved, but adaptability to complex real-world environments deteriorates
Solution Approach 1:
The variance threshold mechanism provides feedback to the reinforcement learning agent about environmental uncertainty. When variance exceeds the threshold, the system signals that the current state is more complex or unpredictable than expected, prompting human intervention. This feedback loop allows the agent to operate autonomously in familiar deterministic patterns while adapting to complex real-world situations by escalating to human expertise when needed.
Data Source
AI summary
Mechanism for selecting an action to be taken by a reinforcement learning agent in an environment, including: determining a first variance for a first state of the environment, wherein the first variance is based on reinforcement learning using a hardware processor; determining that the first variance meets a threshold; in response to determining that the first variance meets the threshold: requesting an identification of a first action to be taken by the agent from a human; and receiving the identification of the first action; and causing the first action to be taken by the agent.