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Found 6 papers, 1 papers with code
A Deeper Understanding of State-Based Critics in Multi-Agent Reinforcement Learning
Centralized Training for Decentralized Execution, where training is done in a centralized offline fashion, has become a popular solution paradigm in Multi-Agent Reinforcement Learning.
Reconciling Rewards with Predictive State Representations
We show that there is a mismatch between optimal POMDP policies and the optimal PSR policies derived from approximate rewards.
Unbiased Asymmetric Reinforcement Learning under Partial Observability
In partially observable reinforcement learning, offline training gives access to latent information which is not available during online training and/or execution, such as the system state.
Active Goal Recognition
We propose to combine goal recognition with other observer tasks in order to obtain \emph{active goal recognition} (AGR).
Identification of Unmodeled Objects from Symbolic Descriptions
Successful human-robot cooperation hinges on each agent's ability to process and exchange information about the shared environment and the task at hand.
On a Family of Decomposable Kernels on Sequences
In many applications data is naturally presented in terms of orderings of some basic elements or symbols.
