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Found 15 papers, 1 papers with code
Competitive Facility Location under Random Utilities and Routing Constraints
To tackle this problem, we explore three types of valid cuts, namely, outer-approximation and submodular cuts to handle the nonlinear objective function, as well as sub-tour elimination cuts to address the complex routing constraints.
SubIQ: Inverse Soft-Q Learning for Offline Imitation with Suboptimal Demonstrations
Most of the existing offline IL methods developed for this setting are based on behavior cloning or distribution matching, where the aim is to match the occupancy distribution of the imitation policy with that of the expert policy.
Imitate the Good and Avoid the Bad: An Incremental Approach to Safe Reinforcement Learning
In an exhaustive set of experiments, we demonstrate that our approach is able to outperform top benchmark approaches for solving Constrained RL problems, with respect to expected cost, CVaR cost, or even unknown cost constraints.
Inverse Factorized Q-Learning for Cooperative Multi-agent Imitation Learning
This paper concerns imitation learning (IL) (i. e, the problem of learning to mimic expert behaviors from demonstrations) in cooperative multi-agent systems.
Mimicking To Dominate: Imitation Learning Strategies for Success in Multiagent Competitive Games
Training agents in multi-agent competitive games presents significant challenges due to their intricate nature.
Network-based Representations and Dynamic Discrete Choice Models for Multiple Discrete Choice Analysis
In this paper, to address this, we propose a random utility maximization (RUM) based model that considers each subset of choice alternatives as a composite alternative, where individuals choose a subset according to the RUM framework.
Solving Richly Constrained Reinforcement Learning through State Augmentation and Reward Penalties
Constrained Reinforcement Learning has been employed to enforce safety constraints on policy through the use of expected cost constraints.
Imitating Opponent to Win: Adversarial Policy Imitation Learning in Two-player Competitive Games
The core idea of our new algorithm is to create a new imitator to imitate the victim agent's policy while the adversarial policy will be trained not only based on interactions with the victim agent but also based on feedback from the imitator to forecast victim's intention.
Weighted Maximum Entropy Inverse Reinforcement Learning
We study inverse reinforcement learning (IRL) and imitation learning (IM), the problems of recovering a reward or policy function from expert's demonstrated trajectories.
Scalable Distributional Robustness in a Class of Non Convex Optimization with Guarantees
Distributionally robust optimization (DRO) has shown lot of promise in providing robustness in learning as well as sample based optimization problems.
Estimation of Recursive Route Choice Models with Incomplete Trip Observations
This work concerns the estimation of recursive route choice models in the situation that the trip observations are incomplete, i. e., there are unconnected links (or nodes) in the observations.
Robust Entropy-regularized Markov Decision Processes
Stochastic and soft optimal policies resulting from entropy-regularized Markov decision processes (ER-MDP) are desirable for exploration and imitation learning applications.
A Relation Analysis of Markov Decision Process Frameworks
We show that the entropy-regularized MDP is equivalent to a stochastic MDP model, and is strictly subsumed by the general regularized MDP.
Inverse Reinforcement Learning with Missing Data
We consider the problem of recovering an expert's reward function with inverse reinforcement learning (IRL) when there are missing/incomplete state-action pairs or observations in the demonstrated trajectories.
Generalized Maximum Causal Entropy for Inverse Reinforcement Learning
We consider the problem of learning from demonstrated trajectories with inverse reinforcement learning (IRL).
