Zobrazeno 1 - 10
of 265
pro vyhledávání: '"Kamgarpour, Maryam"'
An open problem in linear quadratic (LQ) games has been characterizing the Nash equilibria. This problem has renewed relevance given the surge of work on understanding the convergence of learning algorithms in dynamic games. This paper investigates s
Externí odkaz:
http://arxiv.org/abs/2410.12544
We present a policy iteration algorithm for the infinite-horizon N-player general-sum deterministic linear quadratic dynamic games and compare it to policy gradient methods. We demonstrate that the proposed policy iteration algorithm is distinct from
Externí odkaz:
http://arxiv.org/abs/2410.03106
Inverse reinforcement learning (IRL) aims to infer a reward from expert demonstrations, motivated by the idea that the reward, rather than the policy, is the most succinct and transferable description of a task [Ng et al., 2000]. However, the reward
Externí odkaz:
http://arxiv.org/abs/2406.01793
This paper investigates the convergence time of log-linear learning to an $\epsilon$-efficient Nash equilibrium (NE) in potential games. In such games, an efficient NE is defined as the maximizer of the potential function. Previous literature provide
Externí odkaz:
http://arxiv.org/abs/2405.15497
It is a common practice in the current literature of electricity markets to use game-theoretic approaches for strategic price bidding. However, they generally rely on the assumption that the strategic bidders have prior knowledge of rival bids, eithe
Externí odkaz:
http://arxiv.org/abs/2404.03314
Given a dataset of expert demonstrations, inverse reinforcement learning (IRL) aims to recover a reward for which the expert is optimal. This work proposes a model-free algorithm to solve entropy-regularized IRL problem. In particular, we employ a st
Externí odkaz:
http://arxiv.org/abs/2403.16829
We present a chance-constrained model predictive control (MPC) framework under Gaussian mixture model (GMM) uncertainty. Specifically, we consider the uncertainty that arises from predicting future behaviors of moving obstacles, which may exhibit mul
Externí odkaz:
http://arxiv.org/abs/2401.03799
Autor:
Ouhamma, Reda, Kamgarpour, Maryam
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. Previous works demonstrated convergence to a Nash equilibrium in this setting using double
Externí odkaz:
http://arxiv.org/abs/2312.08008
Autor:
Ni, Tingting, Kamgarpour, Maryam
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. Motivated by the application o
Externí odkaz:
http://arxiv.org/abs/2312.00561
Autor:
Maddux, Anna M., Kamgarpour, Maryam
Publikováno v:
International Conference on Artificial Intelligence and Statistics 2024
We consider the problem of learning to play a repeated contextual game with unknown reward and unknown constraints functions. Such games arise in applications where each agent's action needs to belong to a feasible set, but the feasible set is a prio
Externí odkaz:
http://arxiv.org/abs/2310.14685