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Found 6 papers, 2 papers with code
Prediction with Incomplete Data under Agnostic Mask Distribution Shift
We focus on the case where the underlying joint distribution of complete features and label is invariant, but the missing pattern, i. e., mask distribution may shift agnostically between training and testing.
User-Oriented Robust Reinforcement Learning
Specifically, we define a new User-Oriented Robustness (UOR) metric for RL, which allocates different weights to the environments according to user preference and generalizes the max-min robustness metric.
DeCOM: Decomposed Policy for Constrained Cooperative Multi-Agent Reinforcement Learning
In addition, with such modularization, the training algorithm of DeCOM separates the original constrained optimization into an unconstrained optimization on reward and a constraints satisfaction problem on costs.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Networked Time Series Prediction with Incomplete Data via Generative Adversarial Network
An important task in such applications is to predict the future values of a NETS based on its historical values and the underlying graph.
Spatio-Temporal Hierarchical Adaptive Dispatching for Ridesharing Systems
Thus, in this paper, we exploit adaptive dispatching intervals to boost the platform's profit under a guarantee of the maximum passenger waiting time.
CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario
The most commonly used open-source traffic simulator SUMO is, however, not scalable to large road network and large traffic flow, which hinders the study of reinforcement learning on traffic scenarios.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
