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Found 6 papers, 2 papers with code
A Model-Based Solution to the Offline Multi-Agent Reinforcement Learning Coordination Problem
Training multiple agents to coordinate is an essential problem with applications in robotics, game theory, economics, and social sciences.
Learning to Guide and to Be Guided in the Architect-Builder Problem
ABIG results in a low-level, high-frequency, guiding communication protocol that not only enables an architect-builder pair to solve the task at hand, but that can also generalize to unseen tasks.
Regularized Inverse Reinforcement Learning
Inverse Reinforcement Learning (IRL) aims to facilitate a learner's ability to imitate expert behavior by acquiring reward functions that explain the expert's decisions.
Adversarial Soft Advantage Fitting: Imitation Learning without Policy Optimization
Adversarial Imitation Learning alternates between learning a discriminator -- which tells apart expert's demonstrations from generated ones -- and a generator's policy to produce trajectories that can fool this discriminator.
Scalable Multi-Agent Inverse Reinforcement Learning via Actor-Attention-Critic
Multi-agent adversarial inverse reinforcement learning (MA-AIRL) is a recent approach that applies single-agent AIRL to multi-agent problems where we seek to recover both policies for our agents and reward functions that promote expert-like behavior.
Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning
Finally, we analyze the effects of our proposed methods on the policies that our agents learn and show that our methods successfully enforce the qualities that we propose as proxies for coordinated behaviors.
