Add to ORKGRobots are often highly non-linear dynamical systems with many degrees of freedom, making solving motion problems computationally challenging. One solution has been reinforcement learning (RL), which learns through experimentation to automatically perform the near-optimal motions that complete a task. However, high-dimensional problems and task formulation often prove challenging for RL. We address these problems with PrEference Appraisal Reinforcement Learning (PEARL), which solves Preference Balancing Tasks (PBTs). PBTs define a problem as a set of preferences that the system must balance to achieve a goal. The method is appropriate for acceleration-controlled systems with continuous state-space and either discrete or continuous action sp...
Practical implementations of deep reinforcement learning (deep RL) have been challenging due to an a...
Reinforcement learning (RL) techniques optimize the accumulated long-term reward of a suitably chose...
The field of deep reinforcement learning has seen major successes recently, achieving superhuman per...
Robots are often highly non-linear dynamical systems with many degrees of freedom, making solving mo...
International audienceThis paper focuses on reinforcement learning (RL) with limited prior knowledge...
This work tackles in-situ robotics: the goal is to learn a policy while the robot operates in the re...
Abstract. This paper focuses on reinforcement learning (RL) with lim-ited prior knowledge. In the do...
Abstract. This paper focuses on reinforcement learning (RL) with lim-ited prior knowledge. In the do...
As most action generation problems of autonomous robots can be phrased in terms of sequential decisi...
The current reward learning from human preferences could be used to resolve complex reinforcement le...
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2...
This paper makes a first step toward the integration of two subfields of machine learning, namely pr...
Mobile robots are increasingly being employed for performing complex tasks in dynamic environments. ...
A summary of the state-of-the-art reinforcement learning in robotics is given, in terms of both algo...
International audienceAimed at on-board robot training, an approach hybridizing active preference le...
Practical implementations of deep reinforcement learning (deep RL) have been challenging due to an a...
Reinforcement learning (RL) techniques optimize the accumulated long-term reward of a suitably chose...
The field of deep reinforcement learning has seen major successes recently, achieving superhuman per...
Robots are often highly non-linear dynamical systems with many degrees of freedom, making solving mo...
International audienceThis paper focuses on reinforcement learning (RL) with limited prior knowledge...
This work tackles in-situ robotics: the goal is to learn a policy while the robot operates in the re...
Abstract. This paper focuses on reinforcement learning (RL) with lim-ited prior knowledge. In the do...
Abstract. This paper focuses on reinforcement learning (RL) with lim-ited prior knowledge. In the do...
As most action generation problems of autonomous robots can be phrased in terms of sequential decisi...
The current reward learning from human preferences could be used to resolve complex reinforcement le...
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2...
This paper makes a first step toward the integration of two subfields of machine learning, namely pr...
Mobile robots are increasingly being employed for performing complex tasks in dynamic environments. ...
A summary of the state-of-the-art reinforcement learning in robotics is given, in terms of both algo...
International audienceAimed at on-board robot training, an approach hybridizing active preference le...
Practical implementations of deep reinforcement learning (deep RL) have been challenging due to an a...
Reinforcement learning (RL) techniques optimize the accumulated long-term reward of a suitably chose...
The field of deep reinforcement learning has seen major successes recently, achieving superhuman per...