Deep Reinforcement
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Dueling Network Architectures for Deep Reinforcement …
proceedings.mlr.pressOver 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
DRN: A Deep Reinforcement Learning Framework for News ...
www.personal.psu.eduOur 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
Asynchronous Methods for Deep Reinforcement Learning
proceedings.mlr.pressAsynchronous 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-
Soft Actor-Critic: Off-Policy Maximum Entropy Deep ...
arxiv.orgModel-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
Lecture 14: Reinforcement Learning
cs231n.stanford.eduIf 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 ...