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Found 4 papers, 0 papers with code
Personalizing Task-oriented Dialog Systems via Zero-shot Generalizable Reward Function
In this work, we propose a novel framework, P-ToD, to personalize task-oriented dialog systems capable of adapting to a wide range of user profiles in an unsupervised fashion using a zero-shot generalizable reward function.
Using Non-Stationary Bandits for Learning in Repeated Cournot Games with Non-Stationary Demand
We found that using our proposed method, agents are able to swiftly change their course of action according to the changes in demand, and they also engage in collusive behavior in many simulations.
Modelling Cournot Games as Multi-agent Multi-armed Bandits
We investigate the use of a multi-agent multi-armed bandit (MA-MAB) setting for modeling repeated Cournot oligopoly games, where the firms acting as agents choose from the set of arms representing production quantity (a discrete value).
Multi-agent Reinforcement Learning for Decentralized Stable Matching
Along with conventional stable matching case where agents have strictly ordered preferences, we check the applicability of our approach for stable matching with incomplete lists and ties.
