Hybrid Reinforcement Learning for Risk-Aware Autonomous Driving
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Solution Overview
Problem
Reinforcement learning systems for autonomous driving face challenges in determining subsequent actions due to the increasing complexity of state and action information, particularly in partially observable Markov decision processes, where uncertainty in state-action value functions complicates decision-making.
Innovation Solution
A hybrid reinforcement learning system combines model-free and model-based solutions, using a neural network to determine actions based on current and previous states, with a model-based approach selecting actions based on reward and risk when further analysis is desired, and a metric-based selection when further analysis is not needed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If model-free reinforcement learning is used to determine actions, then the system can handle complex state information, but uncertainty in state-action value functions complicates decision-making
Solution Approach 1:
The patent combines model-free reinforcement learning (which handles complex states well) with model-based reinforcement learning (which provides reliable decision-making through transition models). The hybrid architecture merges the strengths of both approaches: the model-free component processes complex state information while the model-based component provides reliable action selection through learned transition models, resolving the contradiction between adaptability and reliability
2Measurement precision
If further analysis is performed on action sets, then action selection accuracy improves, but computational time increases
Solution Approach 1:
The system performs partial analysis by selectively applying model-based reinforcement learning only when needed. A determination module assesses whether further analysis is required based on the current state and action set characteristics. When the action set is clear or time-critical, the system selects actions without extensive analysis, thereby reducing computational time while maintaining adequate accuracy
3Reliability
If hybrid reinforcement learning is used to balance exploration and exploitation, then decision-making quality improves, but system complexity increases
Solution Approach 1:
The hybrid reinforcement learning system is segmented into distinct functional modules: a model-free RL component for initial action suggestions, a model-based RL component for refined decision-making, and a determination module for selecting which component to use. This segmentation manages complexity by organizing the hybrid architecture into manageable, specialized sub-systems that can be independently developed and tuned
Data Source
AI summary
A method includes determining a current state of an environment of an autonomous agent, such as a vehicle. The method also includes determining, via a first neural network, a set of actions based on the current state. The method further includes determining whether further analysis of the set of actions is desired. The method selects an action from the set of actions using a model-based solution based on a reward and a risk of the action when further analysis is desired. The method also includes selecting the action from the set of actions according to a metric when further analysis is not desired. The method controls the autonomous agent to perform the selected action.