Exploration in deep reinforcement learning: A survey
This paper reviews exploration techniques in deep reinforcement learning. Exploration
techniques are of primary importance when solving sparse reward problems. In sparse …
techniques are of primary importance when solving sparse reward problems. In sparse …
Efficient Reinforcement Learning from Demonstration via Bayesian Network‐Based Knowledge Extraction
Y Zhang, Y Lan, Q Fang, X Xu, J Li… - Computational …, 2021 - Wiley Online Library
Reinforcement learning from demonstration (RLfD) is considered to be a promising
approach to improve reinforcement learning (RL) by leveraging expert demonstrations as …
approach to improve reinforcement learning (RL) by leveraging expert demonstrations as …
Actor-critic reinforcement learning for bidding in bilateral negotiation
F Arslan, R Aydoğan - Turkish Journal of Electrical …, 2022 - journals.tubitak.gov.tr
Designing an effective and intelligent bidding strategy is one of the most compelling
research challenges in automated negotiation, where software agents negotiate with each …
research challenges in automated negotiation, where software agents negotiate with each …
A train trajectory optimization method based on the safety reinforcement learning with a relaxed dynamic reward
L Cheng, J Cao, X Yang, W Wang, Z Zhou - Discover Applied Sciences, 2024 - Springer
Train trajectory optimization (TTO) is an effective way to address energy consumption in rail
transit. Reinforcement learning (RL), an excellent optimization method, has been used to …
transit. Reinforcement learning (RL), an excellent optimization method, has been used to …