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Reinforcement learning to adjust parametrized motor primitives to new situations
(Springer Science+Business Media, 2012-11)
Humans manage to adapt learned movements very quickly to new situations by generalizing learned behaviors from similar situations. In contrast, robots currently often need to re-learn the complete movement. In this paper, ...
Deep reinforcement based power allocation for the max-min optimization in non-orthogonal multiple access
(IEEE, 2020)
NOMA is a radio access technique that multiplexes several users over the frequency resource and provides high throughput and fairness among different users. The maximization of the minimum the data-rate, also known as ...
SDNHAS: An SDN-Enabled architecture to optimize qoe in http adaptive streaming
(IEEE, 2017-10)
HTTP adaptive streaming (HAS) is receiving much attention from both industry and academia as it has become the de facto approach to stream media content over the Internet. Recently, we proposed a streaming architecture ...
Modeling the development of infant imitation using inverse reinforcement learning
(IEEE, 2018-09)
Little is known about the computational mechanisms of how imitation skills develop along with infant sensorimotor learning. In robotics, there are several well developed frameworks for imitation learning or so called ...
Learning to exploit passive compliance for energy-efficient gait generation on a compliant humanoid
(Springer Nature, 2019-01)
Modern humanoid robots include not only active compliance but also passive compliance. Apart from improved safety and dependability, availability of passive elements, such as springs, opens up new possibilities for improving ...
High-level features for resource economy and fast learning in skill transfer
Abstraction is an important aspect of intelligence which enables agents to construct robust representations for effective and efficient decision making. Although, deep neural networks are proven to be effective learning ...
Maintaining connectivity for multi-UAV multi-target search using reinforcement learning
(Association for Computing Machinery, Inc, 2023-10-30)
We propose a dynamic path planner that uses a multi-Agent reinforcement learning (MARL) model with novel reward functions for multi-drone search and rescue (SAR) missions. We design a mission environment where a multi-drone ...
BoB: Bandwidth prediction for real-time communications using heuristic and reinforcement learning
(IEEE, 2023)
Bandwidth prediction is critical in any Real-time Communication (RTC) service or application. This component decides how much media data can be sent in real time. Subsequently, the video and audio encoder dynamically adapts ...
ACNMP: skill transfer and task extrapolation through learning from demonstration and reinforcement learning via representation sharing
(ML Research Press, 2020)
To equip robots with dexterous skills, an effective approach is to first transfer the desired skill via Learning from Demonstration (LfD), then let the robot improve it by self-exploration via Reinforcement Learning (RL). ...
Language inference with multi-head automata through reinforcement learning
(IEEE, 2020)
The purpose of this paper is to use reinforcement learning to model learning agents which can recognize formal languages. Agents are modeled as simple multi-head automaton, a new model of finite automaton that uses multiple ...
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