Reinforcement learning methods provide a framework that enables the design of learning policies for general networks. There have been two main lines of work on reinforcement learning methods: model-free reinforcement learning (e.g. Q-learning [4], policy gradient [5]) and model-based reinforce-ment learning (e.g., UCRL [6], PSRL [7]). In this .

IEOR 8100: Reinforcement learning Lecture 1: Introduction By Shipra Agrawal 1 Introduction to reinforcement learning What is reinforcement learning? Reinforcement learning is characterized by an agent continuously interacting and learning from a stochastic environment. Imagine a robot movin

2.3 Deep Reinforcement Learning: Deep Q-Network 7 that the output computed is consistent with the training labels in the training set for a given image. [1] 2.3 Deep Reinforcement Learning: Deep Q-Network Deep Reinforcement Learning are implementations of Reinforcement Learning methods that use Deep Neural Networks to calculate the optimal policy.

In this section, we present related work and background concepts such as reinforcement learning and multi-objective reinforcement learning. 2.1 Reinforcement Learning A reinforcement learning (Sutton and Barto, 1998) environment is typically formalized by means of a Markov decision process (MDP). An MDP can be described as follows. Let S fs 1 .

learning techniques, such as reinforcement learning, in an attempt to build a more general solution. In the next section, we review the theory of reinforcement learning, and the current efforts on its use in other cooperative multi-agent domains. 3. Reinforcement Learning Reinforcement learning is often characterized as the

Meta-reinforcement learning. Meta reinforcement learn-ing aims to solve a new reinforcement learning task by lever-aging the experience learned from a set of similar tasks. Currently, meta-reinforcement learning can be categorized into two different groups. The first group approaches (Duan et al. 2016; Wang et al. 2016; Mishra et al. 2018) use an

Introduction to Reinforcement Learning Model-based Reinforcement Learning Markov Decision Process Planning by Dynamic Programming Model-free Reinforcement Learning On-policy SARSA Off-policy Q-learning

Using a retaining wall as a case-study, the performance of two commonly used alternative reinforcement layouts (of which one is wrong) are studied and compared. Reinforcement Layout 1 had the main reinforcement (from the wall) bent towards the heel in the base slab. For Reinforcement Layout 2, the reinforcement was bent towards the toe.