When Does Adaptive Guidance Help? Belief-Aware Privileged Distillation for Autonomous Driving Under Partial Observability
arXiv:2605.26155v1 Announce Type: cross Abstract: Guided Soft Actor-Critic (GSAC) distills knowledge from a privileged full-state teacher to a partial-observation student for autonomous driving, but uses a fixed distillation coefficient lambda regardless of the agent's uncertainty. We present Belief-Aware GSAC (BA-GSAC), which modulates lambda via ensemble disagreement, and use it as a testbed for a systematic empirical study asking: when does adaptive guidance actually help? Evaluating five strategies (fixed lambda in {0.01, 0.1}, adaptive, linear decay, and vanilla SAC) across three POMDP di
The continuous advancements in AI and robotics, coupled with the increasing complexity of real-world autonomous systems like self-driving cars, necessitate more sophisticated and robust guidance mechanisms.
Improving autonomous driving's ability to handle partial observability through adaptive guidance significantly enhances safety and reliability, paving the way for broader deployment and public trust.
The explicit introduction of belief-aware, adaptive guidance as a superior method to fixed distillation coefficients, suggesting a more robust approach to AI model training for autonomous systems.
- · Autonomous driving companies
- · AI researchers in reinforcement learning
- · Consumers of self-driving technology
- · Developers relying solely on fixed-parameter distillation methods
Adaptive guidance models will become standard in autonomous system development, improving real-world performance.
Increased efficiency and safety in autonomous vehicles could accelerate the transition to fully self-driving cars.
The principles of belief-aware adaptive guidance might be applied to other AI agent domains, leading to more resilient agentic systems.
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