Agents
PatchGame: Learning to Signal Mid-level Patches in Referential Games
We study a referential game (a type of signaling game) where two agents communicate with each other via a discrete bottleneck to achieve a common goal.
Sample-Efficient Learning of Stackelberg Equilibria in General-Sum Games
Real world applications such as economics and policy making often involve solving multi-agent games with two unique features: (1) The agents are inherently asymmetric and partitioned into leaders and followers; (2) The agents have different reward functions, thus the game is general-sum . The majority of existing results in this field focuses on either symmetric solution concepts (e.g.
Sample-Efficient Learning of Stackelberg Equilibria in General-Sum Games
Real world applications such as economics and policy making often involve solving multi-agent games with two unique features: (1) The agents are inherently asymmetric and partitioned into leaders and followers; (2) The agents have different reward functions, thus the game is general-sum . The majority of existing results in this field focuses on either symmetric solution concepts (e.g.
Learning General World Models in a Handful of Reward-Free Deployments Yingchen Xu UCL, FAIR Jack Parker-Holder University of Oxford Aldo Pacchiano Microsoft Research Philip J. Ball
Combining these two properties, we introduce the reward-free deployment efficiency setting, a new paradigm for RL research.
Proximal Learning With Opponent-Learning Awareness
Learning With Opponent-Learning A wareness (LOLA) (Foerster et al. [2018a]) is a multi-agent reinforcement learning algorithm that typically learns reciprocity-based
Learn what matters: cross-domain imitation learning with task-relevant embeddings
We jointly train the learner agent's policy and learn
