Reinforcement learning (RL) has shown promising results for real-time co...
Resource scheduling and allocation is a critical component of many high
...
Transformers were originally proposed as a sequence-to-sequence model fo...
This paper is a technical overview of DeepMind and Google's recent work ...
Reinforcement learning (RL) techniques have been developed to optimize
i...
We present a hybrid industrial cooling system model that embeds analytic...
We consider the offline constrained reinforcement learning (RL) problem,...
This paper addresses the problem of policy selection in domains with abu...
Standard dynamics models for continuous control make use of feedforward
...
Off-policy evaluation (OPE) holds the promise of being able to leverage
...
Many real-world physical control systems are required to satisfy constra...
Offline reinforcement learning (RL purely from logged data) is an import...
Offline methods for reinforcement learning have the potential to help br...
Reinforcement learning (RL) has proven its worth in a series of artifici...
We address the problem of deploying a reinforcement learning (RL) agent ...