Towards continual reinforcement learning: A review and perspectives
In this article, we aim to provide a literature review of different formulations and approaches
to continual reinforcement learning (RL), also known as lifelong or non-stationary RL. We …
to continual reinforcement learning (RL), also known as lifelong or non-stationary RL. We …
Large sequence models for sequential decision-making: a survey
Transformer architectures have facilitated the development of large-scale and general-
purpose sequence models for prediction tasks in natural language processing and computer …
purpose sequence models for prediction tasks in natural language processing and computer …
A survey of zero-shot generalisation in deep reinforcement learning
The study of zero-shot generalisation (ZSG) in deep Reinforcement Learning (RL) aims to
produce RL algorithms whose policies generalise well to novel unseen situations at …
produce RL algorithms whose policies generalise well to novel unseen situations at …
Diffusion model is an effective planner and data synthesizer for multi-task reinforcement learning
Diffusion models have demonstrated highly-expressive generative capabilities in vision and
NLP. Recent studies in reinforcement learning (RL) have shown that diffusion models are …
NLP. Recent studies in reinforcement learning (RL) have shown that diffusion models are …
Gnfactor: Multi-task real robot learning with generalizable neural feature fields
It is a long-standing problem in robotics to develop agents capable of executing diverse
manipulation tasks from visual observations in unstructured real-world environments. To …
manipulation tasks from visual observations in unstructured real-world environments. To …
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 …
Multi-task learning with deep neural networks: A survey
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 …
Mt-opt: Continuous multi-task robotic reinforcement learning at scale
General-purpose robotic systems must master a large repertoire of diverse skills to be useful
in a range of daily tasks. While reinforcement learning provides a powerful framework for …
in a range of daily tasks. While reinforcement learning provides a powerful framework for …
Multi-task learning as a bargaining game
In Multi-task learning (MTL), a joint model is trained to simultaneously make predictions for
several tasks. Joint training reduces computation costs and improves data efficiency; …
several tasks. Joint training reduces computation costs and improves data efficiency; …