Transferable Reinforcement Learning via Probabilistic Latent Embeddings and Dynamic Policy Adaptation for Sim-to-Real Deployment
arXiv:2605.27659v1 Announce Type: new Abstract: Due to limited resources and public safety concerns, deep reinforcement learning (RL) agents for many cyber-physical systems (e.g., autonomous vehicles) are first trained in simulators. However, when deployed in real world environments, they often suffer from performance degradation or safety violations because of the inevitable Sim2Real gap. Existing zero-shot approaches, such as robust safe RL and domain randomization, mitigate this issue but typically at the cost of degraded performance or residual safety risks when experiencing unmodeled syst
The increasing sophistication of reinforcement learning in simulated environments necessitates better methods for real-world deployment, especially for safety-critical applications.
Improving the 'Sim2Real gap' is critical for the practical and safe application of advanced AI in physical systems, directly impacting commercial viability and safety.
This research outlines a pathway to more robust and transferable deep reinforcement learning agents, potentially accelerating the deployment of autonomous systems from simulation to reality.
- · Autonomous vehicle developers
- · Robotics industry
- · AI safety researchers
- · Logistics and manufacturing
- · Companies reliant on bespoke real-world training for AI
- · Inefficient simulation-to-reality transfer methodologies
Wider adoption of advanced AI in cyber-physical systems due to enhanced reliability.
Reduced development costs and accelerated timelines for autonomous system deployment across various industries.
New regulatory frameworks challenged by rapidly evolving, highly adaptive AI systems with less human oversight.
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Read at arXiv cs.LG