Search Results for author:
Found 24 papers, 7 papers with code
Decomposing Control Lyapunov Functions for Efficient Reinforcement Learning
In this paper, we build from existing work that reshapes the reward function in RL by introducing a Control Lyapunov Function (CLF), which is demonstrated to reduce the sample complexity.
Auto-Encoding Bayesian Inverse Games
When multiple agents interact in a common environment, each agent's actions impact others' future decisions, and noncooperative dynamic games naturally capture this coupling.
An Investigation of Time Reversal Symmetry in Reinforcement Learning
We observe that utilizing the structure of time reversal in an MDP allows every environment transition experienced by an agent to be transformed into a feasible reverse-time transition, effectively doubling the number of experiences in the environment.
Encouraging Inferable Behavior for Autonomy: Repeated Bimatrix Stackelberg Games with Observations
As a converse result, we also provide a game where the required number of interactions is lower bounded by a function of the desired inferability loss.
Symbolic Regression on Sparse and Noisy Data with Gaussian Processes
To overcome this, we combine Gaussian process regression with a sparse identification of nonlinear dynamics (SINDy) method to denoise the data and identify nonlinear dynamical equations.
Connected Autonomous Vehicle Motion Planning with Video Predictions from Smart, Self-Supervised Infrastructure
Connected autonomous vehicles (CAVs) promise to enhance safety, efficiency, and sustainability in urban transportation.
Active Inverse Learning in Stackelberg Trajectory Games
We formulate an inverse learning problem in a Stackelberg game between a leader and a follower, where each player's action is the trajectory of a dynamical system.
Enabling Efficient, Reliable Real-World Reinforcement Learning with Approximate Physics-Based Models
We focus on developing efficient and reliable policy optimization strategies for robot learning with real-world data.
Scenario-Game ADMM: A Parallelized Scenario-Based Solver for Stochastic Noncooperative Games
To accommodate this, we decompose the approximated game into a set of smaller games with few constraints for each sampled scenario, and propose a decentralized, consensus-based ADMM algorithm to efficiently compute a generalized Nash equilibrium (GNE) of the approximated game.
Soft-Bellman Equilibrium in Affine Markov Games: Forward Solutions and Inverse Learning
Markov games model interactions among multiple players in a stochastic, dynamic environment.
GrAVITree: Graph-based Approximate Value Function In a Tree
In this paper, we introduce GrAVITree, a tree- and sampling-based algorithm to compute a near-optimal value function and corresponding feedback policy for indefinite time-horizon, terminal state-constrained nonlinear optimal control problems.
Robust Forecasting for Robotic Control: A Game-Theoretic Approach
In order to model real-world factors affecting future forecasts, we introduce the notion of an adversary, which perturbs observed historical time series to increase a robot's ultimate control cost.
Back to the Future: Efficient, Time-Consistent Solutions in Reach-Avoid Games
We study the class of reach-avoid dynamic games in which multiple agents interact noncooperatively, and each wishes to satisfy a distinct target criterion while avoiding a failure criterion.
Approximate Solutions to a Class of Reachability Games
In this paper, we present a method for finding approximate Nash equilibria in a broad class of reachability games.
Technical Report: Adaptive Control for Linearizable Systems Using On-Policy Reinforcement Learning
This paper proposes a framework for adaptively learning a feedback linearization-based tracking controller for an unknown system using discrete-time model-free policy-gradient parameter update rules.
Feedback Linearization for Unknown Systems via Reinforcement Learning
We present a novel approach to control design for nonlinear systems which leverages model-free policy optimization techniques to learn a linearizing controller for a physical plant with unknown dynamics.
An Iterative Quadratic Method for General-Sum Differential Games with Feedback Linearizable Dynamics
Iterative linear-quadratic (ILQ) methods are widely used in the nonlinear optimal control community.
Systems and Control Computer Science and Game Theory Multiagent Systems Robotics Systems and Control
Efficient Iterative Linear-Quadratic Approximations for Nonlinear Multi-Player General-Sum Differential Games
We benchmark our method in a three-player general-sum simulated example, in which it takes < 0. 75 s to identify a solution and < 50 ms to solve warm-started subproblems in a receding horizon.
Systems and Control Robotics Systems and Control
Safely Probabilistically Complete Real-Time Planning and Exploration in Unknown Environments
We present a new framework for motion planning that wraps around existing kinodynamic planners and guarantees recursive feasibility when operating in a priori unknown, static environments.
Robotics Systems and Control
A Successive-Elimination Approach to Adaptive Robotic Sensing
We study an adaptive source seeking problem, in which a mobile robot must identify the strongest emitter(s) of a signal in an environment with background emissions.
Towards Distributed Energy Services: Decentralizing Optimal Power Flow with Machine Learning
We consider distribution systems with multiple controllable Distributed Energy Resources (DERs) and present a data-driven approach to learn control policies for each DER to reconstruct and mimic the solution to a centralized OPF problem from solely locally available information.
Probabilistically Safe Robot Planning with Confidence-Based Human Predictions
In order to safely operate around humans, robots can employ predictive models of human motion.
Planning, Fast and Slow: A Framework for Adaptive Real-Time Safe Trajectory Planning
Motion planning is an extremely well-studied problem in the robotics community, yet existing work largely falls into one of two categories: computationally efficient but with few if any safety guarantees, or able to give stronger guarantees but at high computational cost.
Systems and Control Computer Science and Game Theory
Fully Decentralized Policies for Multi-Agent Systems: An Information Theoretic Approach
Learning cooperative policies for multi-agent systems is often challenged by partial observability and a lack of coordination.
