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Interactive imitation learning in robotics: A survey
Interactive Imitation Learning in Robotics: A Survey Page 1 Interactive Imitation Learning in
Robotics: A Survey Page 2 Other titles in Foundations and Trends® in Robotics A Survey on …
Robotics: A Survey Page 2 Other titles in Foundations and Trends® in Robotics A Survey on …
An interactive framework for learning continuous actions policies based on corrective feedback
The main goal of this article is to present COACH (COrrective Advice Communicated by
Humans), a new learning framework that allows non-expert humans to advise an agent …
Humans), a new learning framework that allows non-expert humans to advise an agent …
Training an actor-critic reinforcement learning controller for arm movement using human-generated rewards
Functional Electrical Stimulation (FES) employs neuroprostheses to apply electrical current
to the nerves and muscles of individuals paralyzed by spinal cord injury to restore voluntary …
to the nerves and muscles of individuals paralyzed by spinal cord injury to restore voluntary …
Hierarchical control of traffic signals using Q-learning with tile coding
Multi-agent systems are rapidly growing as powerful tools for Intelligent Transportation
Systems (ITS). It is desirable that traffic signals control, as a part of ITS, is performed in a …
Systems (ITS). It is desirable that traffic signals control, as a part of ITS, is performed in a …
Leveraging sub-optimal data for human-in-the-loop reinforcement learning
C Muslimani, ME Taylor - ar** humans in the loop: Teaching via feedback in continuous action space environments
Interactive Reinforcement Learning (IntRL) allows human teachers to accelerate the
learning process of Reinforcement Learning (RL) robots. However, IntRL has largely been …
learning process of Reinforcement Learning (RL) robots. However, IntRL has largely been …
Probabilistic neural network training procedure based on Q(0)-learning algorithm in medical data classification
In this article, an iterative procedure is proposed for the training process of the probabilistic
neural network (PNN). In each stage of this procedure, the Q (0)-learning algorithm is …
neural network (PNN). In each stage of this procedure, the Q (0)-learning algorithm is …
Real-time prediction learning for the simultaneous actuation of multiple prosthetic joints
Integrating learned predictions into a prosthetic control system promises to enhance multi-
joint prosthesis use by amputees. In this article, we present a preliminary study of different …
joint prosthesis use by amputees. In this article, we present a preliminary study of different …
Learning from demonstrations and human evaluative feedbacks: Handling sparsity and imperfection using inverse reinforcement learning approach
Programming by demonstrations is one of the most efficient methods for knowledge transfer
to develop advanced learning systems, provided that teachers deliver abundant and correct …
to develop advanced learning systems, provided that teachers deliver abundant and correct …