Interactive Learning of Temporal Features for Control: Shaping Policies and State Representations From Human Feedback

Current ongoing industry revolution demands more flexible products, including robots in household environments and medium-scale factories. Such robots should be able to adapt to new conditions and environments and be programmed with ease. As an example, let us suppose that there are robot manipulators working on an industrial production line and...

Interactive Learning with Corrective Feedback for Policies Based on Deep Neural Networks

Deep Reinforcement Learning (DRL) has become a powerful strategy to solve complex decision making problems based on Deep Neural Networks (DNNs). However, it is highly data demanding, so unfeasible in physical systems for most applications. In this work, we approach an alternative Interactive Machine Learning (IML) strategy for training DNN...

Reinforcement learning of motor skills using Policy Search and human corrective advice

Robot learning problems are limited by physical constraints, which make learning successful policies for complex motor skills on real systems unfeasible. Some reinforcement learning methods, like Policy Search, offer stable convergence toward locally optimal solutions, whereas interactive machine learning or learning-from-demonstration methods...

Continuous control for high-dimensional state spaces: An interactive learning approach

Deep Reinforcement Learning (DRL) has become a powerful methodology to solve complex decision-making problems. However, DRL has several limitations when used in real-world problems (e.g., robotics applications). For instance, long training times are required and cannot be accelerated in contrast to simulated environments, and reward functions...

A fast hybrid reinforcement learning framework with human corrective feedback

Reinforcement Learning agents can be supported by feedback from human teachers in the learning loop that guides the learning process. In this work we propose two hybrid strategies of Policy Search Reinforcement Learning and Interactive Machine Learning that benefit from both sources of information, the cost function and the human corrective...

Decentralized Reinforcement Learning of robot behaviors

A multi-agent methodology is proposed for Decentralized Reinforcement Learning (DRL) of individual behaviors in problems where multi-dimensional action spaces are involved. When using this methodology, sub-tasks are learned in parallel by individual agents working toward a common goal. In addition to proposing this methodology, three specific...