Search Results for author:
Found 11 papers, 6 papers with code
Policy Synthesis and Reinforcement Learning for Discounted LTL
The difficulty of manually specifying reward functions has led to an interest in using linear temporal logic (LTL) to express objectives for reinforcement learning (RL).
Robust Subtask Learning for Compositional Generalization
We formulate the problem as a two agent zero-sum game in which the adversary picks the sequence of subtasks.
Chordal Sparsity for SDP-based Neural Network Verification
Neural networks are central to many emerging technologies, but verifying their correctness remains a major challenge.
Specification-Guided Learning of Nash Equilibria with High Social Welfare
Our empirical evaluation demonstrates that our algorithm computes equilibrium policies with high social welfare, whereas state-of-the-art baselines either fail to compute Nash equilibria or compute ones with comparatively lower social welfare.
Chordal Sparsity for Lipschitz Constant Estimation of Deep Neural Networks
Lipschitz constants of neural networks allow for guarantees of robustness in image classification, safety in controller design, and generalizability beyond the training data.
Compositional Reinforcement Learning from Logical Specifications
Our approach then incorporates reinforcement learning to learn neural network policies for each edge (sub-task) within a Dijkstra-style planning algorithm to compute a high-level plan in the graph.
Abstract Value Iteration for Hierarchical Reinforcement Learning
We propose a novel hierarchical reinforcement learning framework for control with continuous state and action spaces.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
A Composable Specification Language for Reinforcement Learning Tasks
Reinforcement learning is a promising approach for learning control policies for robot tasks.
Verisig: verifying safety properties of hybrid systems with neural network controllers
This paper presents Verisig, a hybrid system approach to verifying safety properties of closed-loop systems using neural networks as controllers.
Systems and Control
SyGuS-Comp 2017: Results and Analysis
Syntax-Guided Synthesis (SyGuS) is the computational problem of finding an implementation f that meets both a semantic constraint given by a logical formula phi in a background theory T, and a syntactic constraint given by a grammar G, which specifies the allowed set of candidate implementations.
SyGuS-Comp 2016: Results and Analysis
Syntax-Guided Synthesis (SyGuS) is the computational problem of finding an implementation f that meets both a semantic constraint given by a logical formula $\varphi$ in a background theory T, and a syntactic constraint given by a grammar G, which specifies the allowed set of candidate implementations.
