… hours and sometimes over a day for industry-standard electronic design automation (EDA)
tools to evaluate a single design). Even after breaking the problem into more manageable …

… (RL) has become a highly successful framework for learning in Markov decision … reinforcement
learning and categorize these methods in four different ways: (i) Transition robust designs …

… To enable progress towards de-novo molecular design, we introduce MOLGYM, an … design
tasks along with baselines. In our experiments, we show that our agent can efficiently learn to …

… Our work is different from the previous work in that we propose to use a reinforcement learning
agent to … In recent years, reinforcement learning (RL) agents have demonstrated immense …

… reinforcement learning (RL) have improved many areas but are not yet widely adopted in
economic policy design, mechanism design, or … RL framework for policy design in which agents …

… reinforcement learning framework for traffic signal control, MetaLight, which is built upon the
gradient-based metareinforcement learning … scenarios need be carefully designed. (2) We …

… Reinforcement learning Reinforcement learning is applicable to optimization problems that
can be modeled as sequential decision-making processes, ie, Markov Decision Processes (…

… of each major component of the Reinforcement Learning, from the selection of algorithm,
state, action, value approximation to the design of environment. Such a comprehensive …

… is designed to explain the science behind reinforcement … Many newcomers to reinforcement
learning may be … in the popular media: reinforcement learning is often described as an “agent…

… We describe the key design choices of learning algorithms for inventory applications. … term
reinforcement learning. Deep reinforcement learning refers to the use of neural networks in …