Undirected Networks
Two-way Deconfounder for Off-policy Evaluation in Causal Reinforcement Learning Shuguang Y u
Before deploying any newly developed policy, it is important to assess its impact. In many high-stakes domains, it is risky or unethical to implement such policies directly for online evaluation.
One Demonstration is Enough to Learn Robot Policies
Sequential decision-making problems typically require significant human supervision and data.
A Supplementary Material This Supplementary Material section provides additional details and discussions to complement our
C Results in ALFRED's T ask and New T asks
Transformers Represent Belief State Geometry in their Residual Stream
In Section 3, we verify that this geometry is linearly represented in the residual stream of transformers.
Task-aware world model learning with meta weighting via bi-level optimization
Aligning the world model with the environment for the agent's specific task is
IMP-MARL: a Suite of Environments for Large-scale Infrastructure Management Planning via MARL *Pascal Leroy
In IMP, a multi-component engineering system is subject to a risk of failure due to its components' damage condition.