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Found 6 papers, 2 papers with code
DPMAC: Differentially Private Communication for Cooperative Multi-Agent Reinforcement Learning
Communication lays the foundation for cooperation in human society and in multi-agent reinforcement learning (MARL).
Best-of-three-worlds Analysis for Linear Bandits with Follow-the-regularized-leader Algorithm
Follow-the-regularized-leader (FTRL) is another type of popular algorithm that can adapt to different environments.
Comparison-based Conversational Recommender System with Relative Bandit Feedback
To enable users to provide comparative preferences during conversational interactions, we propose a novel comparison-based conversational recommender system.
Simultaneously Learning Stochastic and Adversarial Bandits under the Position-Based Model
This work studies the OLTR problem in both stochastic and adversarial environments under the position-based model (PBM).
Differentially Private Temporal Difference Learning with Stochastic Nonconvex-Strongly-Concave Optimization
Temporal difference (TD) learning is a widely used method to evaluate policies in reinforcement learning.
Conservative Contextual Combinatorial Cascading Bandit
We propose the novel \emph{conservative contextual combinatorial cascading bandit ($C^4$-bandit)}, a cascading online learning game which incorporates the conservative mechanism.
