Agents
Towards Optimal Off-Policy Evaluation for Reinforcement Learning with Marginalized Importance Sampling
Tengyang Xie, Yifei Ma, Yu-Xiang Wang
Solving OPE is often the starting point in many RL applications. To tackle the problem of OPE, the idea of importance sampling (IS) corrects the mismatch in the distributions under the behavior policy and target policy.
Neural Dynamic Policies for End-to-End Sensorimotor Learning
The current dominant paradigm in sensorimotor control, whether imitation or reinforcement learning, is to train policies directly in raw action spaces such as torque, joint angle, or end-effector position.
Certifying Strategyproof Auction Networks Michael J. Curry
Optimal auctions maximize a seller's expected revenue subject to individual rationality and strategyproofness for the buyers.
Delay and Cooperation in Nonstochastic Linear Bandits
This paper offers a nearly optimal algorithm for online linear optimization with delayed bandit feedback.
Modelling the Dynamics of Multiagent Q-Learning in Repeated Symmetric Games: a Mean Field Theoretic Approach
Shuyue Hu, Chin-wing Leung, Ho-fung Leung
Neural Information Processing Systems http://nips.cc/
Response about the significance and originality
The theoretical contributions of our work and the works [Mguni et al., AAAI'18; Mguni et al., AAMAS'19] mentioned
A Structured Prediction Approach for Generalization in Cooperative Multi-Agent Reinforcement Learning
Nicolas Carion, Nicolas Usunier, Gabriel Synnaeve, Alessandro Lazaric
Neural Information Processing Systems http://nips.cc/
Regret-Based Pruning in Extensive-Form Games
Neural Information Processing Systems http://nips.cc/