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Found 4 papers, 1 papers with code
Generative Modelling of Stochastic Actions with Arbitrary Constraints in Reinforcement Learning
Many problems in Reinforcement Learning (RL) seek an optimal policy with large discrete multidimensional yet unordered action spaces; these include problems in randomized allocation of resources such as placements of multiple security resources and emergency response units, etc.
Inverse Factorized Q-Learning for Cooperative Multi-agent Imitation Learning
This paper concerns imitation learning (IL) (i. e, the problem of learning to mimic expert behaviors from demonstrations) in cooperative multi-agent systems.
Mimicking To Dominate: Imitation Learning Strategies for Success in Multiagent Competitive Games
Training agents in multi-agent competitive games presents significant challenges due to their intricate nature.
CounterNet: End-to-End Training of Prediction Aware Counterfactual Explanations
Prior techniques for generating CF explanations suffer from two major limitations: (i) all of them are post-hoc methods designed for use with proprietary ML models -- as a result, their procedure for generating CF explanations is uninformed by the training of the ML model, which leads to misalignment between model predictions and explanations; and (ii) most of them rely on solving separate time-intensive optimization problems to find CF explanations for each input data point (which negatively impacts their runtime).
