Autonomous Driving Reward Modeling With Goal-State Embeddings
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Solution Overview
Problem
Existing reinforcement learning algorithms for autonomous driving face challenges in crafting a dense and well-shaped reward function, particularly in complex scenarios, leading to suboptimal action policies due to sparse reward functions that lack generalizability and require extensive expert knowledge.
Innovation Solution
Utilize foundation models to generate image-based and text-based embeddings, calculating cosine similarity between the current state and goal state to define a reward function, facilitating goal-oriented reinforcement learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional reinforcement learning algorithms are used with sparse reward functions, then the system requires extensive expert knowledge to craft reward functions, but the reward function lacks generalizability and leads to suboptimal action policies
Solution Approach 1:
The system uses foundation models to automatically generate reward functions from natural language descriptions, allowing the RL agent to self-adapt to new tasks without requiring expert intervention. The foundation model encodes both the current state and goal state, and automatically computes the reward function through similarity comparison, enabling the system to serve itself in reward function design.
Solution Approach 2:
The foundation model serves multiple functions: it acts as both the state encoder and goal encoder, and generates the reward function for different tasks using the same architecture. This universal approach allows the same system to handle diverse autonomous driving tasks (highway driving, urban driving, parking) with a single reward function generation mechanism, greatly improving generalizability.
2Reliability
If dense and well-shaped reward functions are crafted for complex scenarios, then the action policy optimization improves, but the reward function design becomes extremely complex and task-specific
Solution Approach 1:
The patent replaces the manual, expert-driven mechanical process of crafting reward functions with an automated AI-based system. Foundation models automatically encode states and generate reward functions through similarity comparisons, substituting the complex human expert process with an automated computational approach that produces dense and well-shaped reward functions consistently across different tasks.
Solution Approach 2:
The system changes the parameter representation from hand-crafted numerical reward functions to embedding-based representations. By transforming states and goals into vector embeddings and computing similarity, the system dynamically adjusts reward parameters based on the semantic relationship between current and goal states, producing appropriate reward signals for complex scenarios without manual tuning.
3Ease of manufacture
If foundation models are used to generate reward functions from natural language, then the reward design becomes easy and generalizable, but the computational processing complexity increases
Solution Approach 1:
The foundation model acts as an intermediary between natural language descriptions and the reinforcement learning reward function. It translates human-readable goal descriptions into computational reward signals through embedding encoding and similarity comparison, making the reward design process accessible to non-experts while managing computational complexity through efficient vector operations.
Data Source
AI summary
Methods and systems for optimizing an action policy of an autonomous vehicle machine learning model. Images are generated corresponding to an environment about a vehicle. These images are passed through an image encoder to generate image-based embeddings of the current state of the vehicle. A text prompt representing a goal of the autonomous vehicle is passed through a text encoder to generate text-based embeddings of the goal. A similarity score is determined, representing a similarity between the image-based embeddings of the current state and the text-based embeddings of the goal. A reinforcement learning model for a closed-loop autonomous driving task is executed, with the similarity score used as the reward function. An action policy corresponding to a control of the vehicle is optimized based on the reward function.