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Found 6 papers, 2 papers with code
Private Wasserstein Distance with Random Noises
Wasserstein distance is a principle measure of data divergence from a distributional standpoint.
Meta Generative Flow Networks with Personalization for Task-Specific Adaptation
Multi-task reinforcement learning and meta-reinforcement learning have been developed to quickly adapt to new tasks, but they tend to focus on tasks with higher rewards and more frequent occurrences, leading to poor performance on tasks with sparse rewards.
Multi-agent Policy Reciprocity with Theoretical Guarantee
We then define an adjacency space for mismatched states and design a plug-and-play module for value iteration, which enables agents to infer more precise returns.
CFlowNets: Continuous Control with Generative Flow Networks
Generative flow networks (GFlowNets), as an emerging technique, can be used as an alternative to reinforcement learning for exploratory control tasks.
On the Convergence Theory of Meta Reinforcement Learning with Personalized Policies
Modern meta-reinforcement learning (Meta-RL) methods are mainly developed based on model-agnostic meta-learning, which performs policy gradient steps across tasks to maximize policy performance.
Multiple Intelligent Reflecting Surface aided Multi-user Weighted Sum-Rate Maximization using Manifold Optimization
Intelligent reflecting surface (IRS) are able to amend radio propagation condition tasks on account of its functional properties in phase shift optimizing.
