Search results with tag "Deep reinforcement"
Rainbow: Combining Improvements in Deep Reinforcement …
arxiv.orgDeep reinforcement learning and DQN. Large state and/or action spaces make it intractable to learn Q value estimates for each state and action pair independently. In deep reinforcement learning, we represent the various com-ponents of agents, such as policies ˇ(s;a) or values q(s;a), with deep (i.e., multi-layer) neural networks. The parameters
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
Neural Networks and Deep Learning - ndl.ethernet.edu.et
ndl.ethernet.edu.et3. Advanced topics in neural networks: A lot of the recent success of deep learning is a result of the specialized architectures for various domains, such as recurrent neural networks and convolutional neural networks. Chapters 7 and 8 discuss recurrent and convolutional neural networks. Several advanced topics like deep reinforcement learn-
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
车联网边缘计算环境下基于深度强化学习的分布 式服务卸载方法
cjc.ict.ac.cnA Deep Reinforcement Learning-BasedDistributed Service Offloading Method ... average service latency by 0.4% to 20.4% compared with four exiting service offloading methods in different IoV environments, proving the effectiveness and efficiency of D-SOAC. ... asynchronous advantage actor-critic 1 引言 ...