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Found 9 papers, 0 papers with code
Pursuit Winning Strategies for Reach-Avoid Games with Polygonal Obstacles
The pursuers cooperate to protect a convex region from the evaders who try to reach the region.
Inverse Reinforcement Learning with Unknown Reward Model based on Structural Risk Minimization
A simplistic model is less likely to contain the real reward function, while a model with high complexity leads to substantial computation cost and risks overfitting.
Observation-based Optimal Control Law Learning with LQR Reconstruction
Designing controllers to generate various trajectories has been studied for years, while recently, recovering an optimal controller from trajectories receives increasing attention.
Multiplayer Homicidal Chauffeur Reach-Avoid Games: A Pursuit Enclosure Function Approach
We propose a cooperative strategy for the pursuers based on subgames for multiple pursuers against one evader and optimal task allocation.
A Stochastic Surveillance Stackelberg Game: Co-Optimizing Defense Placement and Patrol Strategy
Stochastic patrol routing is known to be advantageous in adversarial settings; however, the optimal choice of stochastic routing strategy is dependent on a model of the adversary.
Reinforcement Learning with Temporal-Logic-Based Causal Diagrams
We study a class of reinforcement learning (RL) tasks where the objective of the agent is to accomplish temporally extended goals.
Revisiting Estimation Bias in Policy Gradients for Deep Reinforcement Learning
We show that, despite such state distribution shift, the policy gradient estimation bias can be reduced in the following three ways: 1) a small learning rate; 2) an adaptive-learning-rate-based optimizer; and 3) KL regularization.
Robust Pandemic Control Synthesis with Formal Specifications: A Case Study on COVID-19 Pandemic
We provide simulation results in two different scenarios for robust control of the COVID-19 pandemic: one for vaccination control, and another for shield immunity control, with the model parameters estimated from data in Lombardy, Italy.
Policy Evaluation and Seeking for Multi-Agent Reinforcement Learning via Best Response
With this knowledge we propose a class of perturbed SBRD with the following property: only policies with maximum metric are observed with nonzero probability for a broad class of stochastic games with finite memory.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
