Agent Control Using Value Refinement for Sparse-Reward Navigation
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Solution Overview
Problem
Reinforcement Learning (RL) tasks are challenging due to sparse reward signals, leading to inefficient training and potential failure in achieving desired behaviors, especially in robotic navigation scenarios, and existing methods like Value Iteration (VI)-RL require precise high-level state space abstractions that are infeasible for many real-world tasks.
Innovation Solution
A Value Refinement Network (VRN) approach that utilizes a neural network to refine incomplete or sub-optimal high-level state space abstractions, incorporating continuous robot states and sensor measurements for more flexible and efficient control, allowing for better scaling and adaptability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If Value Iteration (VI)-RL approach with high-level state space abstraction is used, then data efficiency is improved, but the method becomes infeasible for real-world tasks requiring precise abstractions
Solution Approach 1:
The patent replaces the manual, mechanical process of designing high-level state space abstractions with an automated neural network system. The VRN learns to generate appropriate abstractions from raw sensor data, eliminating the need for manual discretization and abstraction design that makes VI-RL infeasible for real-world tasks.
Solution Approach 2:
The patent changes the parameter representation from fixed, manually-defined discrete state spaces to continuous, learned representations. The neural network dynamically determines which state variables are relevant and how to combine them, transforming the rigid parameter structure of VI-RL into a flexible, adaptive system.
2Device complexity
If sparse reward signals are used in reinforcement learning, then the task complexity is reduced, but training efficiency deteriorates and may fail to achieve desired behaviors
Solution Approach 1:
The patent introduces an intermediary neural network (VRN) that bridges the gap between sparse environmental rewards and the policy gradient updates. The VRN learns a value function that provides dense, informative gradients even when environmental rewards are sparse, enabling efficient training without increasing control system complexity.
3Measurement precision
If complete high-level state space abstractions are required for VI-RL, then control accuracy is improved, but the method becomes inapplicable to tasks where such abstractions are infeasible
Solution Approach 1:
The patent creates a universal control framework that can handle diverse tasks without requiring task-specific high-level abstractions. The neural network automatically adapts to different environments and tasks by learning relevant state representations from data, making the system applicable to a wide range of problems where manual abstraction would be infeasible.
Data Source
AI summary
A method for controlling an agent. The method includes obtaining numerical values of a first and second set of state variables, which together represent a current full state of the agent, and the numerical values of the first set of state variables represent a current partial state of the robot; determining a state value prior comprising, for potential subsequent partial states following the current partial state, an evaluation of the subsequent partial states in terms of achieving a goal to be attained by the agent; supplying an input comprising a local crop of the state value prior and the numerical values of the second set of state variables representing, together with the numerical values of the first set of state variables, the current full state to a neural network configured to output an evaluation of control actions and controlling the agent in accordance with control signals.