Knowledge-integrated machine learning for materials: lessons from gameplaying and robotics
As materials researchers increasingly embrace machine-learning (ML) methods, it is natural
to wonder what lessons can be learned from other fields undergoing similar developments …
to wonder what lessons can be learned from other fields undergoing similar developments …
Hierarchical reinforcement learning: A comprehensive survey
Hierarchical Reinforcement Learning (HRL) enables autonomous decomposition of
challenging long-horizon decision-making tasks into simpler subtasks. During the past …
challenging long-horizon decision-making tasks into simpler subtasks. During the past …
Multi-game decision transformers
A longstanding goal of the field of AI is a method for learning a highly capable, generalist
agent from diverse experience. In the subfields of vision and language, this was largely …
agent from diverse experience. In the subfields of vision and language, this was largely …
Learning agile soccer skills for a bipedal robot with deep reinforcement learning
We investigated whether deep reinforcement learning (deep RL) is able to synthesize
sophisticated and safe movement skills for a low-cost, miniature humanoid robot that can be …
sophisticated and safe movement skills for a low-cost, miniature humanoid robot that can be …
Conflict-averse gradient descent for multi-task learning
The goal of multi-task learning is to enable more efficient learning than single task learning
by sharing model structures for a diverse set of tasks. A standard multi-task learning …
by sharing model structures for a diverse set of tasks. A standard multi-task learning …
How to train your robot with deep reinforcement learning: lessons we have learned
Deep reinforcement learning (RL) has emerged as a promising approach for autonomously
acquiring complex behaviors from low-level sensor observations. Although a large portion of …
acquiring complex behaviors from low-level sensor observations. Although a large portion of …
Sim-to-real transfer in deep reinforcement learning for robotics: a survey
W Zhao, JP Queralta… - 2020 IEEE symposium …, 2020 - ieeexplore.ieee.org
Deep reinforcement learning has recently seen huge success across multiple areas in the
robotics domain. Owing to the limitations of gathering real-world data, ie, sample inefficiency …
robotics domain. Owing to the limitations of gathering real-world data, ie, sample inefficiency …
Multi-task learning with deep neural networks: A survey
M Crawshaw - arXiv preprint arXiv:2009.09796, 2020 - arxiv.org
Multi-task learning (MTL) is a subfield of machine learning in which multiple tasks are
simultaneously learned by a shared model. Such approaches offer advantages like …
simultaneously learned by a shared model. Such approaches offer advantages like …
Transfer learning in deep reinforcement learning: A survey
Reinforcement learning is a learning paradigm for solving sequential decision-making
problems. Recent years have witnessed remarkable progress in reinforcement learning …
problems. Recent years have witnessed remarkable progress in reinforcement learning …