We lifted 1 functions out of this paper's own repositories and ran 0 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| copy not recorded | — | 0 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| perturb_actions | Not yet run | this paper's copy was not recorded; identical code first harvested from befelix/safe_learning pointer only · get_code("8a5fdb040f931560") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Learning algorithms have shown considerable prowess in simulation by allowing robots to adapt to uncertain environments and improve their performance. However, such algorithms are rarely used in practice on safety-critical systems, since the learned policy typically does not yield any safety guarantees. That is, the required exploration may cause physical harm to the robot or its environment. In this paper, we present a method to learn accurate safety certificates for nonlinear, closed-loop dynamical systems. Specifically, we construct a neural network Lyapunov function and a training algorithm that adapts it to the shape of the largest safe region in the state space. The algorithm relies only on knowledge of inputs and outputs of the dynamics, rather than on any specific model structure. We demonstrate our method by learning the safe region of attraction for a simulated inverted pendulum. Furthermore, we discuss how our method can be used in safe learning algorithms together with statistical models of dynamical systems.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1808.00924")
get_code_for_paper("1808.00924")
have("1808.00924")
Connect an agent — have() is free.