RAMAC: Multimodal Risk-Aware Offline Reinforcement Learning and the Role of Behavior Regularization
arXiv:2510.02695v3 Announce Type: replace-cross Abstract: In safety-critical domains where online data collection is infeasible, offline reinforcement learning (RL) is attractive only if policies achieve high returns without catastrophic lower-tail risk. Prior work on risk-averse offline RL achieves safety at the cost of either (i) value/model-based pessimism or (ii) restricted policy classes that limit expressiveness, whereas diffusion/flow-based expressive generative policies have largely been used in risk-neutral settings. We introduce \textbf{Risk-Aware Multimodal Actor-Critic (RAMAC)}, a
The increasing deployment of AI in real-world critical applications, coupled with growing computational resources, necessitates robust risk mitigation techniques in offline reinforcement learning to ensure safe and reliable operation.
This research addresses a fundamental limitation in offline reinforcement learning by developing methods to ensure safety without sacrificing policy expressiveness, which is crucial for the adoption of AI in high-stakes environments.
The introduction of RAMAC provides a new framework that allows for both high returns and strong safety guarantees in offline RL, potentially enabling more widespread and confident deployment of autonomous systems.
- · AI developers
- · Safety-critical autonomous systems
- · Robotics
- · Healthcare AI
- · Traditional risk-averse RL methods
- · High-risk, unvalidated AI deployments
Improved safety and reliability of AI systems trained on offline data.
Accelerated adoption of AI in highly regulated and safety-conscious industries.
Enhanced public trust and reduced regulatory friction for advanced AI applications, potentially shifting development paradigms towards 'safety-first' by default.
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Read at arXiv cs.AI