The first part of a two-part series of papers provides a survey on recent advances in Deep
Reinforcement Learning (DRL) applications for solving partially observable Markov decision …

We study the benefit of sharing representations among tasks to enable the effective use of
deep neural networks in Multi-Task Reinforcement Learning. We leverage the assumption …

This paper studies representation learning for multi-task linear bandits and multi-task
episodic RL with linear value function approximation. We first consider the setting where we …

We study how representation learning can improve the efficiency of bandit problems. We
study the setting where we play $ T $ linear bandits with dimension $ d $ concurrently, and …

Abstract While Deep Reinforcement Learning (DRL) has emerged as a promising approach
to many complex tasks, it remains challenging to train a single DRL agent that is capable of …

We develop a mathematical framework for solving multi-task reinforcement learning (MTRL)
problems based on a type of policy gradient method. The goal in MTRL is to learn a common …

While multitask representation learning has become a popular approach in reinforcement
learning (RL), theoretical understanding of why and when it works remains limited. This …

We give novel algorithms for multi-task and lifelong linear bandits with shared
representation. Specifically, we consider the setting where we play $ M $ linear bandits with …

Training deep reinforcement learning agents complex behaviors in 3D virtual environments
requires significant computational resources. This is especially true in environments with …

Despite the potential of Multi-Agent Reinforcement Learning (MARL) in addressing
numerous complex tasks, training a single team of MARL agents to handle multiple diverse …