arXiv:2606.16236v1 Announce Type: new Abstract: Reinforcement learning (RL) often suffers from performance degradation when deployed in environments that differ from those encountered during training. Existing techniques such as domain randomization (DR) mitigate this, but require access to diverse training environments and full trajectory observability, assumptions that fail in privacy-preserving or restricted scenarios where only scalar performance metrics are available. We propose Generalization via Evolutionary Reward Shaping (GERS), a bilevel optimization approach to improve generalizatio
The paper addresses a core challenge in reinforcement learning (RL) generalization, which is becoming increasingly critical as real-world AI deployments proliferate.
Improving generalization in RL, especially in data-limited or privacy-sensitive scenarios, is crucial for developing robust and deployable AI agents, impacting various industries.
This research proposes a new method for an LLM to learn and adapt to environments more effectively without requiring extensive training data or full observability.
- · AI developers
- · Robotics industry
- · Companies with proprietary data
- · AI-powered automation
- · AI models requiring vast public datasets
- · Traditional domain randomization approaches
- · Sectors heavily reliant on simulation for RL
- · AI solutions with poor generalization
AI agents will exhibit improved performance and adaptability in novel, unseen environments.
This enhanced generalization could accelerate the deployment of autonomous AI systems across diverse and sensitive real-world applications.
More robust and adaptable AI agents might further collapse traditional white-collar workflows by operating effectively in complex, unstructured tasks without human retraining.
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Read at arXiv cs.LG