Results 11 to 20 of about 6,304,901 (198)
PPO-UE: Proximal Policy Optimization via Uncertainty-Aware Exploration [PDF]
CoRR, 2022 Proximal Policy Optimization (PPO) is a highly popular policy-based deep reinforcement learning (DRL) approach. However, we observe that the homogeneous exploration process in PPO could cause an unexpected stability issue in the training phase. To address this issue, we propose PPO-UE, a PPO variant equipped with self-adaptive uncertainty-aware ...
Qisheng Zhang +6 more
core +4 more sources
PPO-ABR: Proximal Policy Optimization based Deep Reinforcement Learning for Adaptive BitRate streaming [PDF]
2023 International Wireless Communications and Mobile Computing (IWCMC), 2023 Providing a high Quality of Experience (QoE) for video streaming in 5G and beyond 5G (B5G) networks is challenging due to the dynamic nature of the underlying network conditions. Several Adaptive Bit Rate (ABR) algorithms have been developed to improve QoE, but most of them are designed based on fixed rules and unsuitable for a wide range of network ...
Mandan Naresh +2 more
core +5 more sources
PPO-Q: Proximal Policy Optimization with Parametrized Quantum Policies or Values
Quantum machine learning (QML), which combines quantum computing with machine learning, is widely believed to hold the potential to outperform traditional machine learning in the era of noisy intermediate-scale quantum (NISQ). As one of the most important types of QML, quantum reinforcement learning (QRL) with parameterized quantum circuits as agents ...
Jin, Yu-Xin +5 more
core +7 more sources
Abstract Proximal Policy Optimization (PPO) is a widely used deep reinforcement-learning algorithm because its clipped surrogate objective offers a practical compromise between update stability and implementation simplicity. However, PPO remains highly sensitive to manually specified hyperparameters, particularly the learning rate ...
Guinan Cai, Jiayu Yao
openaire +2 more sources
Proximal Policy Optimization Based on Self-directed Action Selection [PDF]
Jisuanji kexue, 2021 The optimization algorithm of monotonous improvement of strategy in reinforcement learning is a current research hotspot,and it has achieved good performance in both discrete and continuous control tasks.Proximal policy optimization(PPO)algorithm is a ...
SHEN Yi, LIU Quan
doaj +1 more source
A Functional Clipping Approach for Policy Optimization Algorithms
IEEE Access, 2021 Proximal policy optimization (PPO) has yielded state-of-the-art results in policy search, a subfield of reinforcement learning, with one of its key points being the use of a surrogate objective function to restrict the step size at each policy update ...
Wangshu Zhu, Andre Rosendo
doaj +1 more source
Preferential proximal policy optimization in reinforcement learning [PDF]
, 2023 The Proximal Policy Optimization (PPO), a policy gradient method, excels in reinforcement learning with its ”surrogate” objective function and stochastic gradient ascent.
Balasuntharam, Tamilselvan
core +2 more sources
PTR-PPO: Proximal Policy Optimization with Prioritized Trajectory Replay
CoRR, 2021 On-policy deep reinforcement learning algorithms have low data utilization and require significant experience for policy improvement. This paper proposes a proximal policy optimization algorithm with prioritized trajectory replay (PTR-PPO) that combines on-policy and off-policy methods to improve sampling efficiency by prioritizing the replay of ...
Xingxing Liang +3 more
openaire +2 more sources
Vehicle-Following Control Based on Deep Reinforcement Learning
Applied Sciences, 2022 Intelligent vehicle-following control presents a great challenge in autonomous driving. In vehicle-intensive roads of city environments, frequent starting and stopping of vehicles is one of the important cause of front-end collision accidents. Therefore,
Yong Huang +5 more
doaj +1 more source
Multi-object aerodynamic design optimization using deep reinforcement learning
AIP Advances, 2021 Aerodynamic design optimization is a key aspect in aircraft design. The further evolution of advanced aircraft derivatives requires a powerful optimization toolbox.
Xinyu Hui +5 more
doaj +1 more source