Lecture 14: Reinforcement Learning

If the function approximator is a deep neural network => deep q-learning! Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 14 - May 23, 2017 Solving for the optimal policy: Q-learning 37 ... - Mix of supervised learning and reinforcement learning. Lecture 14 - May 23, 2017 ...

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  Learning, Deep, Reinforcement, Reinforcement learning

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cs231n.stanford.edu

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cs231n.stanford.edu

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cs231n.stanford.edu

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Vector, Matrix, and Tensor Derivatives

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cs231n.stanford.edu

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Lecture 11: Detection and Segmentation

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cs231n.stanford.edu

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cs231n.stanford.edu

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

www.personal.psu.edu

Our deep reinforcement recommender system can be shown as Figure 2. We follow the common terminologies in reinforcement learning [37] to describe the system. In our system, user pool and news pool make up the environment, and our recommendation algorithms play the role of agent. The state is defined as feature

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Dueling Network Architectures for Deep Reinforcement …

proceedings.mlr.press

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 visuomotor policies

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proceedings.mlr.press

Asynchronous Methods for Deep Reinforcement Learning time than previous GPU-based algorithms, using far less resource than massively distributed approaches. The best of the proposed methods, asynchronous advantage actor-critic (A3C), also mastered a variety of continuous motor control tasks as well as learned general strategies for ex-

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arxiv.org

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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