Scenario Generation for Risk-Aware Reinforcement Learning with Probably Approximately Safe Guarantees
Guaranteeing safety is critical to the deployment of reinforcement learning (RL) agents in the real-world, especially as policies learned using deep RL may demonstrate susceptibility to transition perturbations that result in unknown or unsafe behaviour. A method of policy verification is to construct probabilistic barrier-certificates by sampling policy trajectories with respect to safety constraints, thereby demarcating known safe behaviour from unknown behaviour. Obtaining tight upper and lower bounds on the probability of violation of these constraints may be difficult if the policy is susceptible to transition uncertainty or perturbation that places the agent in insufficiently explor...
| Authors | Mohit Prashant, Arvind Easwaran |
|---|---|
| Primary category | cs.LG |
| Published | 3 June 2026 |
| Last updated at source | 5 June 2026 |
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