scholarly journals OpenGraphGym: A Parallel Reinforcement Learning Framework for Graph Optimization Problems

2020 ◽  
pp. 439-452
Author(s):  
Weijian Zheng ◽  
Dali Wang ◽  
Fengguang Song
Keyword(s):  
Reinforcement Learning ◽  
Optimization Problems ◽  
Learning Framework ◽  
Graph Optimization

Enhanced Policy Adaptation Through Directed Explorative Learning

2015 ◽  
Vol 12 (03) ◽  
pp. 1550028 ◽  
Author(s):  
Rok Vuga ◽  
Bojan Nemec ◽  
Aleš Ude
Keyword(s):  
Reinforcement Learning ◽  
Iterative Learning Control ◽  
Optimization Problems ◽  
Learning Algorithm ◽  
Learning Control ◽  
Iterative Learning ◽  
Learning Framework ◽  
Policy Adaptation ◽  
Explorative Learning ◽  
Integrated Policy

In this paper, we propose an integrated policy learning framework that fuses iterative learning control (ILC) and reinforcement learning. Integration is accomplished at the exploration level of the reinforcement learning algorithm. The proposed algorithm combines fast convergence properties of iterative learning control and robustness of reinforcement learning. This way, the advantages of both approaches are retained while overcoming their respective limitations. The proposed approach was verified in simulation and in real robot experiments on three challenging motion optimization problems.

scholarly journals A Reinforcement Learning Framework for Spiking Networks with Dynamic Synapses

2011 ◽  
Vol 2011 ◽  
pp. 1-12 ◽  
Author(s):  
Karim El-Laithy ◽  
Martin Bogdan
Keyword(s):  
Reinforcement Learning ◽  
Spike Timing ◽  
Neural Representation ◽  
Model Parameters ◽  
Learning Framework ◽  
Reference Target ◽  
Wide Range ◽  
Spiking Network ◽  
Dynamic Synapses ◽  
Exclusive Or

An integration of both the Hebbian-based and reinforcement learning (RL) rules is presented for dynamic synapses. The proposed framework permits the Hebbian rule to update the hidden synaptic model parameters regulating the synaptic response rather than the synaptic weights. This is performed using both the value and the sign of the temporal difference in the reward signal after each trial. Applying this framework, a spiking network with spike-timing-dependent synapses is tested to learn the exclusive-OR computation on a temporally coded basis. Reward values are calculated with the distance between the output spike train of the network and a reference target one. Results show that the network is able to capture the required dynamics and that the proposed framework can reveal indeed an integrated version of Hebbian and RL. The proposed framework is tractable and less computationally expensive. The framework is applicable to a wide class of synaptic models and is not restricted to the used neural representation. This generality, along with the reported results, supports adopting the introduced approach to benefit from the biologically plausible synaptic models in a wide range of intuitive signal processing.

Adaptive Reinforcement Learning Framework for NOMA-UAV Networks

2021 ◽  
pp. 1-1
Author(s):  
Syed Khurram Mahmud ◽  
Yuanwei Liu ◽  
Yue Chen ◽  
Kok Keong Chai
Keyword(s):  
Reinforcement Learning ◽  
Learning Framework

scholarly journals A Dual-Critic Reinforcement Learning Framework for Frame-Level Bit Allocation in HEVC/H.265

2021 ◽  
Author(s):  
Yung-Han Ho ◽  
Guo-Lun Jin ◽  
Yun Liang ◽  
Wen-Hsiao Peng ◽  
Xiaobo Li
Keyword(s):  
Reinforcement Learning ◽  
Bit Allocation ◽  
Learning Framework

scholarly journals Priority algorithms for graph optimization problems

2010 ◽  
Vol 411 (1) ◽  
pp. 239-258 ◽  
Author(s):  
Allan Borodin ◽  
Joan Boyar ◽  
Kim S. Larsen ◽  
Nazanin Mirmohammadi
Keyword(s):  
Optimization Problems ◽  
Priority Algorithms ◽  
Graph Optimization

Predicting Human Mobility with Reinforcement-Learning-Based Long-Term Periodicity Modeling

2021 ◽  
Vol 12 (6) ◽  
pp. 1-23
Author(s):  
Shuo Tao ◽  
Jingang Jiang ◽  
Defu Lian ◽  
Kai Zheng ◽  
Enhong Chen
Keyword(s):  
Reinforcement Learning ◽  
Human Mobility ◽  
Recurrent Network ◽  
Mobility Prediction ◽  
Learning Framework ◽  
Temporal Features ◽  
Wide Range ◽  
Spatio Temporal ◽  
Historical Trajectory

Mobility prediction plays an important role in a wide range of location-based applications and services. However, there are three problems in the existing literature: (1) explicit high-order interactions of spatio-temporal features are not systemically modeled; (2) most existing algorithms place attention mechanisms on top of recurrent network, so they can not allow for full parallelism and are inferior to self-attention for capturing long-range dependence; (3) most literature does not make good use of long-term historical information and do not effectively model the long-term periodicity of users. To this end, we propose MoveNet and RLMoveNet. MoveNet is a self-attention-based sequential model, predicting each user’s next destination based on her most recent visits and historical trajectory. MoveNet first introduces a cross-based learning framework for modeling feature interactions. With self-attention on both the most recent visits and historical trajectory, MoveNet can use an attention mechanism to capture the user’s long-term regularity in a more efficient way. Based on MoveNet, to model long-term periodicity more effectively, we add the reinforcement learning layer and named RLMoveNet. RLMoveNet regards the human mobility prediction as a reinforcement learning problem, using the reinforcement learning layer as the regularization part to drive the model to pay attention to the behavior with periodic actions, which can help us make the algorithm more effective. We evaluate both of them with three real-world mobility datasets. MoveNet outperforms the state-of-the-art mobility predictor by around 10% in terms of accuracy, and simultaneously achieves faster convergence and over 4x training speedup. Moreover, RLMoveNet achieves higher prediction accuracy than MoveNet, which proves that modeling periodicity explicitly from the perspective of reinforcement learning is more effective.

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