Sample-efficient Transfer Reinforcement Learning via Adaptive Reward Shaping and Policy-Ratio Reweighting Strategy
arXiv:2606.26527v1 Announce Type: new Abstract: Transfer learning improves policy learning efficiency by reusing knowledge from source tasks, providing a feasible paradigm for safe and efficient autonomous highway lane changing decision-making. Existing methods frequently encounter transfer mismatch induced by distribution shifts between source and target domains, leading to training oscillation and performance decline. Besides, target domain adaptation depends on exploratory interactions, which struggles to guarantee training safety in safety-critical lane changing cases. To tackle these limi
The increasing complexity and safety requirements of real-world AI applications, particularly in autonomous systems, are driving the need for more sample-efficient and robust transfer learning methods.
Improving sample efficiency and mitigating transfer mismatch in reinforcement learning is critical for accelerating the deployment of autonomous systems in safety-critical domains, making development faster and safer.
This advancement enables AI models to learn complex tasks, like autonomous driving, with less training data and greater reliability by effectively transferring knowledge from simulated or related scenarios.
- · Autonomous vehicle developers
- · Logistics and transportation industries
- · AI safety researchers
- · Robotics companies
- · Companies reliant on extensive, costly real-world data collection
- · Development teams with inefficient RL training pipelines
Autonomous systems, such as self-driving cars, can be developed and deployed faster and more safely due to reduced training data requirements and improved performance.
The cost of developing and validating AI for complex, safety-critical applications will decrease, leading to broader adoption and new service models.
This could accelerate the integration of AI agents into various physical domains, potentially changing labor markets in transportation and hazardous industries.
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