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Found 5 papers, 2 papers with code
N-Agent Ad Hoc Teamwork
POAM is a policy gradient, multi-agent reinforcement learning approach to the NAHT problem, that enables adaptation to diverse teammate behaviors by learning representations of teammate behaviors.
Building Minimal and Reusable Causal State Abstractions for Reinforcement Learning
Two desiderata of reinforcement learning (RL) algorithms are the ability to learn from relatively little experience and the ability to learn policies that generalize to a range of problem specifications.
D-Shape: Demonstration-Shaped Reinforcement Learning via Goal Conditioning
While combining imitation learning (IL) and reinforcement learning (RL) is a promising way to address poor sample efficiency in autonomous behavior acquisition, methods that do so typically assume that the requisite behavior demonstrations are provided by an expert that behaves optimally with respect to a task reward.
DM$^2$: Decentralized Multi-Agent Reinforcement Learning for Distribution Matching
The theoretical analysis shows that under certain conditions, each agent minimizing its individual distribution mismatch allows the convergence to the joint policy that generated the target distribution.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
In Pursuit of Interpretable, Fair and Accurate Machine Learning for Criminal Recidivism Prediction
We compared predictive performance and fairness of these models against two methods that are currently used in the justice system to predict pretrial recidivism: the Arnold PSA and COMPAS.
