Agents
Representation Balancing MDPs for Off-policy Policy Evaluation
Yao Liu, Omer Gottesman, Aniruddh Raghu, Matthieu Komorowski, Aldo A. Faisal, Finale Doshi-Velez, Emma Brunskill
We study the problem of off-policy policy evaluation (OPPE) in RL.
Credit Assignment For Collective Multiagent RL With Global Rewards
Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau
Neural Information Processing Systems http://nips.cc/
A Deep Bayesian Policy Reuse Approach Against Non-Stationary Agents
YAN ZHENG, Zhaopeng Meng, Jianye Hao, Zongzhang Zhang, Tianpei Yang, Changjie Fan
There also exist many application scenarios involving multiagent interactions, commonly known as multiagent systems (MAS).
Inequity aversion improves cooperation in intertemporal social dilemmas
Edward Hughes, Joel Z. Leibo, Matthew Phillips, Karl Tuyls, Edgar Dueรฑez-Guzman, Antonio Garcรญa Castaรฑeda, Iain Dunning, Tina Zhu, Kevin McKee, Raphael Koster, Heather Roff, Thore Graepel
Groups of humans are often able to find ways to cooperate with one another in complex, temporally extended social dilemmas.
Object-Oriented Dynamics Predictor
Guangxiang Zhu, Zhiao Huang, Chongjie Zhang
Generalization has been one of the major challenges for learning dynamics models in model-based reinforcement learning.
Learning Attentional Communication for Multi-Agent Cooperation
Neural Information Processing Systems http://nips.cc/
Learning Others' Intentional Models in Multi-Agent Settings Using Interactive POMDPs
Yanlin Han, Piotr Gmytrasiewicz
It extends POMDPs to multi-agent settings by including models of other agents in the state space and forming a hierarchical belief structure. In order to predict other agents' actions using I-POMDPs, we
Multi-Agent Reinforcement Learning via Double Averaging Primal-Dual Optimization
Hoi-To Wai, Zhuoran Yang, Zhaoran Wang, Mingyi Hong
We prove that the proposed algorithm converges to the optimal solution at a global geometric rate.
On Learning Intrinsic Rewards for Policy Gradient Methods
Zeyu Zheng, Junhyuk Oh, Satinder Singh
In this paper we build on the Optimal Rewards Framework of Singh et al. [2010] that defines the optimal intrinsic reward function as one that when used by an RL agent achieves behavior that optimizes the
Depth-Limited Solving for Imperfect-Information Games
Noam Brown, Tuomas Sandholm, Brandon Amos
As a result, depth-limited search algorithms used in single-agent settings and perfect-information games do not apply.