Guided Meta-RL Driving Policy for Diverse Human Behavior
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Solution Overview
Problem
Existing reinforcement learning (RL) methods for autonomous driving struggle to maintain robustness of learned driving policies when faced with diverse human-driven vehicle behaviors, as they often over-fit to simulated behaviors that are not diverse enough.
Innovation Solution
A system using guided meta RL generates RL policies by creating an initial policy for the ego-vehicle, generating guiding policies for social agents based on preferences, and producing a meta-RL guided policy to enhance the robustness of the ego-vehicle's policy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If reinforcement learning policies are trained using fixed simulated social agent policies, then the training process is simple and efficient, but the learned ego-agent policy over-fits to simulated behaviors and lacks robustness to real-world diverse human driving behaviors
Solution Approach 1:
The patent transforms fixed static social agent policies into dynamic diverse policies by introducing preference parameters that control behavior characteristics (aggressive, conservative, cooperative). The social agents' policies are made adjustable and adaptable through these preference settings, allowing the ego-agent to learn robust policies that generalize across different driving styles rather than over-fitting to a single fixed behavior pattern
Solution Approach 2:
The patent changes the parameter space of social agent policies by introducing preference parameters (e.g., aggressiveness, cooperativeness) that define different behavioral modes. By training against multiple social agents with varying preference parameters, the ego-agent learns to handle a broader range of real-world driving scenarios, resolving the over-fitting problem while maintaining training efficiency through parameterized policy generation
2Reliability
If diverse human-like driving behaviors are introduced to improve robustness, then the reliability of the ego-agent policy improves, but the complexity of generating and managing multiple social agent policies increases
Solution Approach 1:
The patent creates a universal social agent policy framework that can generate multiple diverse driving behaviors through a single policy structure with adjustable preference parameters. Instead of implementing separate complex policies for each driving style, the system uses one multi-functional policy generator that adapts its behavior based on preference inputs, reducing overall system complexity while maintaining diversity
Solution Approach 2:
The patent introduces preference parameters as intermediaries between the policy generator and the actual driving behavior. These parameters act as mediators that translate high-level behavioral intentions (aggressive, conservative, cooperative) into concrete policy decisions, simplifying the complexity of directly programming diverse complex policies by providing an abstract control layer
Data Source
AI summary
According to one aspect, a system for generating a reinforcement learning (RL) policy with guided meta RL is provided. The system may include a processor and a memory. The memory may store one or more instructions. The processor may execute one or more of the instructions stored on the memory to perform one or more acts, actions, and/or steps, such as generating an initial RL policy for an ego-vehicle based on an intelligent driver model (IDM), generating a set of RL guiding policies for a set of social agents based on the initial RL policy and a set of preferences, generating a meta-RL guided policy based on the set of RL guiding policies, and generating a RL policy with guided meta RL for the ego-vehicle based on the meta-RL guided policy and the IDM.