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Found 7 papers, 3 papers with code
Looking Ahead to Avoid Being Late: Solving Hard-Constrained Traveling Salesman Problem
To overcome this problem and to have an effective solution against hard constraints, we proposed a novel learning-based method that uses looking-ahead information as the feature to improve the legality of TSP with Time Windows (TSPTW) solutions.
Offline Fictitious Self-Play for Competitive Games
Firstly, unaware of the game structure, it is impossible to interact with the opponents and conduct a major learning paradigm, self-play, for competitive games.
Quantifying Zero-shot Coordination Capability with Behavior Preferring Partners
Current evaluation methods for ZSC capability still need to improve in constructing diverse evaluation partners and comprehensively measuring the ZSC capability.
On Realization of Intelligent Decision-Making in the Real World: A Foundation Decision Model Perspective
The pervasive uncertainty and dynamic nature of real-world environments present significant challenges for the widespread implementation of machine-driven Intelligent Decision-Making (IDM) systems.
Honor of Kings Arena: an Environment for Generalization in Competitive Reinforcement Learning
Compared to other environments studied in most previous work, ours presents new generalization challenges for competitive reinforcement learning.
Efficient Policy Space Response Oracles
Policy Space Response Oracle methods (PSRO) provide a general solution to learn Nash equilibrium in two-player zero-sum games but suffer from two drawbacks: (1) the computation inefficiency due to the need for consistent meta-game evaluation via simulations, and (2) the exploration inefficiency due to finding the best response against a fixed meta-strategy at every epoch.
Improving Dialog Systems for Negotiation with Personality Modeling
In this paper, we explore the ability to model and infer personality types of opponents, predict their responses, and use this information to adapt a dialog agent's high-level strategy in negotiation tasks.
