Add to ORKGSuccessful learning of behaviors in Reinforcement Learning (RL) are often learned tabula rasa, requiring many observations and interactions in the environment. Performing this outside of a simulator, in the real world, often becomes infeasible due to the large amount of interactions needed. This has motivated the use of Transfer Learning for Reinforcement Learning, where learning is accelerated by using experiences from previous learning in related tasks. In this thesis, I explore how we can transfer from a simple single-object pushing policy, to a wide array of non-prehensile rearrangement problems. I then explain how we can model task differences using a low-dimensional latent variable representation to make adaption to novel tasks effici...
The goal of transfer learning algorithms is to utilize knowledge gained in a source task to speed up...
Reinforcement learning is a learning paradigm for solving sequential decision-making problems. Recen...
Consider challenging sim-to-real cases lacking high-fidelity simulators and allowing only 10-20 hard...
Successful learning of behaviors in Reinforcement Learning (RL) are often learned tabula rasa, requi...
Reinforcement learning (RL) is an field of machine learning (ML) which attempts to approach learning...
Learning complex manipulation skills with robotic arms is a challenging problem in Reinforcement Lea...
Transfer learning is an inherent aspect of human learning. When humans learn to perform a task, we r...
In this thesis an issue with common inverse reinforcement learning algorithms is identified, which c...
International audienceTransfer in reinforcement learning is a novel research area that focuses on th...
Agents, physical and virtual entities that interact with theirenvironment, are becoming increasingly...
A major drawback of reinforcement learning (RL) is the slow learning rate. We are interested in spee...
Deep reinforcement learning algorithms typically require large amounts of data to solve a specific p...
Transfer in machine learning is the process of using knowledge learned in a source domain to speedup...
In the past few years, deep reinforcement learning (RL) has shown great potential in learning action...
Deep reinforcement learning has been shown to be a potential alternative to a traditional controller...
The goal of transfer learning algorithms is to utilize knowledge gained in a source task to speed up...
Reinforcement learning is a learning paradigm for solving sequential decision-making problems. Recen...
Consider challenging sim-to-real cases lacking high-fidelity simulators and allowing only 10-20 hard...
Successful learning of behaviors in Reinforcement Learning (RL) are often learned tabula rasa, requi...
Reinforcement learning (RL) is an field of machine learning (ML) which attempts to approach learning...
Learning complex manipulation skills with robotic arms is a challenging problem in Reinforcement Lea...
Transfer learning is an inherent aspect of human learning. When humans learn to perform a task, we r...
In this thesis an issue with common inverse reinforcement learning algorithms is identified, which c...
International audienceTransfer in reinforcement learning is a novel research area that focuses on th...
Agents, physical and virtual entities that interact with theirenvironment, are becoming increasingly...
A major drawback of reinforcement learning (RL) is the slow learning rate. We are interested in spee...
Deep reinforcement learning algorithms typically require large amounts of data to solve a specific p...
Transfer in machine learning is the process of using knowledge learned in a source domain to speedup...
In the past few years, deep reinforcement learning (RL) has shown great potential in learning action...
Deep reinforcement learning has been shown to be a potential alternative to a traditional controller...
The goal of transfer learning algorithms is to utilize knowledge gained in a source task to speed up...
Reinforcement learning is a learning paradigm for solving sequential decision-making problems. Recen...
Consider challenging sim-to-real cases lacking high-fidelity simulators and allowing only 10-20 hard...