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Found 20 papers, 7 papers with code
Convergence of a model-free entropy-regularized inverse reinforcement learning algorithm
Furthermore, with $\mathcal{O}(1/\varepsilon^{4})$ samples we prove that the optimal policy corresponding to the recovered reward is $\varepsilon$-close to the expert policy in total variation distance.
Safe Chance-constrained Model Predictive Control under Gaussian Mixture Model Uncertainty
We present a chance-constrained model predictive control (MPC) framework under Gaussian mixture model (GMM) uncertainty.
Learning Nash Equilibria in Zero-Sum Markov Games: A Single Time-scale Algorithm Under Weak Reachability
We consider decentralized learning for zero-sum games, where players only see their payoff information and are agnostic to actions and payoffs of the opponent.
Interior Point Constrained Reinforcement Learning with Global Convergence Guarantees
We consider discounted infinite horizon constrained Markov decision processes (CMDPs) where the goal is to find an optimal policy that maximizes the expected cumulative reward subject to expected cumulative constraints.
Identifiability and Generalizability in Constrained Inverse Reinforcement Learning
Two main challenges in Reinforcement Learning (RL) are designing appropriate reward functions and ensuring the safety of the learned policy.
Log Barriers for Safe Black-box Optimization with Application to Safe Reinforcement Learning
We introduce a general approach for seeking a stationary point in high dimensional non-linear stochastic optimization problems in which maintaining safety during learning is crucial.
Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation
We consider model-based multi-agent reinforcement learning, where the environment transition model is unknown and can only be learned via expensive interactions with the environment.
Trajectory Planning Under Environmental Uncertainty With Finite-Sample Safety Guarantees
We tackle the problem of trajectory planning in an environment comprised of a set of obstacles with uncertain time-varying locations.
Safe Motion Planning against Multimodal Distributions based on a Scenario Approach
We develop a computationally efficient, scenario-based approach that solves the motion planning problem with high confidence given a quantifiable number of samples from the multimodal distribution.
Contextual Games: Multi-Agent Learning with Side Information
We formulate the novel class of contextual games, a type of repeated games driven by contextual information at each round.
Multi-robot task allocation for safe planning against stochastic hazard dynamics
Computation of a multi-robot optimal control policy is challenging not only because of the complexity of incorporating dynamic uncertainties while planning, but also because of the exponential growth in problem size as a function of number of robots.
Robotics Optimization and Control
A market-based approach for enabling inter-area reserve exchange
Considering the sequential clearing of energy and reserves in Europe, enabling inter-area reserve exchange requires optimally allocating inter-area transmission capacities between these two markets.
Optimization and Control Computer Science and Game Theory
Bandit Learning in Convex Non-Strictly Monotone Games
We address learning Nash equilibria in convex games under the payoff information setting.
Optimization and Control
Learning to Play Sequential Games versus Unknown Opponents
We consider a repeated sequential game between a learner, who plays first, and an opponent who responds to the chosen action.
Safe non-smooth black-box optimization with application to policy search
For safety-critical black-box optimization tasks, observations of the constraints and the objective are often noisy and available only for the feasible points.
Safe Mission Planning under Dynamical Uncertainties
This paper considers safe robot mission planning in uncertain dynamical environments.
Mixed Strategies for Robust Optimization of Unknown Objectives
We consider robust optimization problems, where the goal is to optimize an unknown objective function against the worst-case realization of an uncertain parameter.
Actuator Placement under Structural Controllability using Forward and Reverse Greedy Algorithms
We then obtain performance guarantees for the forward and reverse greedy algorithms applied to the general class of matroid optimization problems by exploiting properties of the objective function such as the submodularity ratio and the curvature.
Optimization and Control
No-Regret Learning in Unknown Games with Correlated Payoffs
We consider the problem of learning to play a repeated multi-agent game with an unknown reward function.
Minimizing Regret of Bandit Online Optimization in Unconstrained Action Spaces
The objective is to minimize the regret, that is, the difference between the sum of the costs she accumulates and that of a static optimal action had she known the sequence of cost functions a priori.
