Using Variable Interval Reinforcement Schedules to Support ...

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Neural Discrete Representation Learning

1 Introduction Recent advances in generative modelling of images [38, 12, 13, 22, 10], audio [37, 26] and videos [20, 11] have yielded impressive samples and applications [24, 18]. At the same time, challenging tasks such as few-shot learning [34], domain adaptation [17], or reinforcement learning [35] heavily

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Learning: Theory and Research

Learning: Theory and Research ... This section provides a brief introduction to each type of learning theory. The theories are treated in four parts: a short historical introduction, a discussion of the view of knowledge presupposed by the theory, an account ... reinforcement. Active assimilation and accommodation of new information to existing ...

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Dueling Network Architectures for Deep Reinforcement …

Introduction Over the past years, deep learning has contributed to dra-matic advances in scalability and performance of machine learning (LeCun et al., 2015). One exciting application is the sequential decision-making setting of reinforcement learning (RL) and control. Notable examples include deep Q-learning (Mnih et al., 2015), deep ...

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DRN: A Deep Reinforcement Learning Framework for News ...

Reinforcement learning, Deep Q-Learning, News recommendation 1 INTRODUCTION The explosive growth of online content and services has provided tons of choices for users. For instance, one of the most popular on-line services, news aggregation services, such as Google News [15] can provide overwhelming volume of content than the amount that

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Soft Actor-Critic: Off-Policy Maximum Entropy Deep ...

1. Introduction Model-free deep reinforcement learning (RL) algorithms have been applied in a range of challenging domains, from games (Mnih et al.,2013;Silver et al.,2016) to robotic control (Schulman et al.,2015). The combination of RL and high-capacity function approximators such as neural networks holds the promise of automating a wide range of

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Maximum Entropy Inverse Reinforcement Learning

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Lecture 1: Introduction to Reinforcement Learning

Lecture 1: Introduction to Reinforcement Learning The RL Problem Reward Examples of Rewards Fly stunt manoeuvres in a helicopter +ve reward for following desired trajectory ve reward for crashing Defeat the world champion at Backgammon += ve reward for winning/losing a game Manage an investment portfolio +ve reward for each $ in bank Control a ...

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