Certifiably Robust Reinforcement Learning through Model-Based Abstract Interpretation

no code implementations • 26 Jan 2023 • Chenxi Yang, Greg Anderson, Swarat Chaudhuri

In each learning iteration, it uses the current version of this model and an external abstract interpreter to construct a differentiable signal for provable robustness.

Adversarial Robustness reinforcement-learning +1

Guiding Safe Exploration with Weakest Preconditions

no code implementations • 28 Sep 2022 • Greg Anderson, Swarat Chaudhuri, Isil Dillig

In reinforcement learning for safety-critical settings, it is often desirable for the agent to obey safety constraints at all points in time, including during training.

Continuous Control reinforcement-learning +2

Neurosymbolic Reinforcement Learning with Formally Verified Exploration

1 code implementation • NeurIPS 2020 • Greg Anderson, Abhinav Verma, Isil Dillig, Swarat Chaudhuri

We present Revel, a partially neural reinforcement learning (RL) framework for provably safe exploration in continuous state and action spaces.

reinforcement-learning Reinforcement Learning (RL) +1

Optimization and Abstraction: A Synergistic Approach for Analyzing Neural Network Robustness

no code implementations • 22 Apr 2019 • Greg Anderson, Shankara Pailoor, Isil Dillig, Swarat Chaudhuri

In recent years, the notion of local robustness (or robustness for short) has emerged as a desirable property of deep neural networks.