… We are interested in investigating embodied cognition within the reinforcement learning (RL…
ability to learn a policy to control the actions of an agent, with a predetermined body design, …

… 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 …

… Our modelbased reinforcement learning approach is similar to these approaches in that
we train a reinforcement learning policy to optimize a model f (x). However, our policy is also …

… 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 …

… carefully-designed experiments that investigate core capabilities of reinforcement learning
(… that capture key issues in the design of general and efficient learning algorithms. Second, to …

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

… Building design and the building management system (BMS) are direct key factors that
affect the comfort level of an occupant. The design of buildings relates to the occupancy level, …

… 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…

… deep reinforcement learning (DRL), which is an indepth combination of artificial neural network
(ANN) and reinforcement learning (RL). … In this work, Q-learning was implemented with a …

… 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 …