Agents
Generating Origin-Destination Matrices in Neural Spatial Interaction Models
Agent-based models (ABMs) are proliferating as decision-making tools across policy areas in transportation, economics, and epidemiology.
Reward Machines for Deep RL in Noisy and Uncertain Environments
Reward Machines provide an automaton-inspired structure for specifying instructions, safety constraints, and other temporally extended reward-worthy behaviour. By exposing the underlying structure of a reward function, they enable the decomposition of an RL task, leading to impressive gains in sample efficiency.
Generalizing Consistency Policy to Visual RL with Prioritized Proximal Experience Regularization
With high-dimensional state spaces, visual reinforcement learning (RL) faces significant challenges in exploitation and exploration, resulting in low sample efficiency and training stability.
Multi-Agent Learning with Heterogeneous Linear Contextual Bandits
UCB, wherein agents cooperatively minimize the group regret under the coordination of a central server.
MARPLE: A Benchmark for Long-Horizon Inference Emily Jin Zhuoyi Huang
Reconstructing past events requires reasoning across long time horizons.