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Found 12 papers, 6 papers with code
Generation of Games for Opponent Model Differentiation
Attackers in the real world are predominantly human actors, and the protection methods often incorporate opponent models to improve the performance when facing humans.
NASimEmu: Network Attack Simulator & Emulator for Training Agents Generalizing to Novel Scenarios
Current frameworks for training offensive penetration testing agents with deep reinforcement learning struggle to produce agents that perform well in real-world scenarios, due to the reality gap in simulation-based frameworks and the lack of scalability in emulation-based frameworks.
Explaining Classifiers Trained on Raw Hierarchical Multiple-Instance Data
Learning from raw data input, thus limiting the need for feature engineering, is a component of many successful applications of machine learning methods in various domains.
Revisiting Game Representations: The Hidden Costs of Efficiency in Sequential Decision-making Algorithms
Recent advancements in algorithms for sequential decision-making under imperfect information have shown remarkable success in large games such as limit- and no-limit poker.
Complexity and Algorithms for Exploiting Quantal Opponents in Large Two-Player Games
This paper aims to analyze and propose scalable algorithms for computing effective and robust strategies against a quantal opponent in normal-form and extensive-form games.
Symbolic Relational Deep Reinforcement Learning based on Graph Neural Networks and Autoregressive Policy Decomposition
We focus on reinforcement learning (RL) in relational problems that are naturally defined in terms of objects, their relations, and object-centric actions.
Classification with Costly Features in Hierarchical Deep Sets
In this work, we extend an existing deep reinforcement learning-based algorithm with hierarchical deep sets and hierarchical softmax, so that it can directly process this data.
Classification with Costly Features as a Sequential Decision-Making Problem
This work focuses on a specific classification problem, where the information about a sample is not readily available, but has to be acquired for a cost, and there is a per-sample budget.
Rethinking Formal Models of Partially Observable Multiagent Decision Making
A second issue is that while EFGs have recently seen significant algorithmic progress, their classical formalization is unsuitable for efficient presentation of the underlying ideas, such as those around decomposition.
Value Functions for Depth-Limited Solving in Zero-Sum Imperfect-Information Games
We provide a formal definition of depth-limited games together with an accessible and rigorous explanation of the underlying concepts, both of which were previously missing in imperfect-information games.
Classification with Costly Features using Deep Reinforcement Learning
We study a classification problem where each feature can be acquired for a cost and the goal is to optimize a trade-off between the expected classification error and the feature cost.
DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker
Poker is the quintessential game of imperfect information, and a longstanding challenge problem in artificial intelligence.
