I study self-learning pricing algorithms and show that they are collusive in market
simulations. To derive a counterfactual that resembles traditional tacit collusion, I conduct …

Drawing upon recent advances in language model alignment, we formulate offline
Reinforcement Learning as a two-stage optimization problem: First pretraining expressive …

We consider the hybrid reinforcement learning setting where the agent has access to both
offline data and online interactive access. While Reinforcement Learning (RL) research …

Multi-Agent Deep Reinforcement Learning (MADRL) was proven efficient in solving complex
problems in robotics or games, yet most of the trained models are hard to interpret. While …

Offline meta reinforcement learning (OMRL) has emerged as a promising approach for
interaction avoidance and strong generalization performance by leveraging pre-collected …

Most reinforcement learning practitioners evaluate their policies with online Monte Carlo
estimators for either hyperparameter tuning or testing different algorithmic design choices …

Offline Reinforcement Learning (RL) has received significant interest due to its ability to
improve policies in previously collected datasets without online interactions. Despite its …

We present a solution to the complex problem of scheduling test operations in a validation
lab or production facility. Our goal is to maximize the utilization of a fleet of test stations and …

The increasing ability to harness quantum, classical, and relativistic scales, together with
fastpaced change in generative AI and quantum computing, suggests the possibility of …

Dynamic resource allocation for network services is pivotal for achieving end-to-end
management objectives. Previous research has demonstrated that Reinforcement Learning …