We investigate learning the equilibria in non-stationary multi-agent sys...
We propose a new model, independent linear Markov game, for multi-agent
...
This paper investigates when one can efficiently recover an approximate ...
Learning Nash equilibria is a central problem in multi-agent systems. In...
This paper presents a systematic study on gap-dependent sample complexit...
This paper considers offline multi-agent reinforcement learning. We prop...
We study what dataset assumption permits solving offline two-player zero...
We propose a model-free reinforcement learning algorithm inspired by the...
In this paper, a novel second-order method called NG+ is proposed. By
fo...
The empirical success of Multi-agent reinforcement learning is encouragi...
It is believed that a model-based approach for reinforcement learning (R...