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MESA: Cooperative Meta-Exploration in Multi-Agent Learning through Exploiting State-Action Space Structure
It learns to explore by first identifying the agents' high-rewarding joint state-action subspace from training tasks and then learning a set of diverse exploration policies to "cover" the subspace.
Fictitious Cross-Play: Learning Global Nash Equilibrium in Mixed Cooperative-Competitive Games
Alternatively, Policy-Space Response Oracles (PSRO) is an iterative framework for learning NE, where the best responses w. r. t.
DeRisk: An Effective Deep Learning Framework for Credit Risk Prediction over Real-World Financial Data
Despite the tremendous advances achieved over the past years by deep learning techniques, the latest risk prediction models for industrial applications still rely on highly handtuned stage-wised statistical learning tools, such as gradient boosting and random forest methods.
A Benchmark for Low-Switching-Cost Reinforcement Learning
A ubiquitous requirement in many practical reinforcement learning (RL) applications, including medical treatment, recommendation system, education and robotics, is that the deployed policy that actually interacts with the environment cannot change frequently.
