257 citations · 1k across the 96 of their papers we have counts for
8 papers · 1 filter
Learning Representations for Pixel-based Control: What Matters and Why?
Manan Tomar, Utkarsh A. Mishra, Amy Zhang +1
Learning representations for pixel-based control has garnered significant attention recently in reinforcement learning. A wide range of methods have been proposed to enable efficie…
A Survey of Zero-shot Generalisation in Deep Reinforcement Learning
Robert Kirk, Amy Zhang, Edward Grefenstette +1
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 deploymen…
Block Contextual MDPs for Continual Learning
Shagun Sodhani, Franziska Meier, Joelle Pineau +1
In reinforcement learning (RL), when defining a Markov Decision Process (MDP), the environment dynamics is implicitly assumed to be stationary. This assumption of stationarity, whi…
Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability
Dibya Ghosh, Jad Rahme, Aviral Kumar +3
Generalization is a central challenge for the deployment of reinforcement learning (RL) systems in the real world. In this paper, we show that the sequential structure of the RL pr…
Provably Efficient Representation Selection in Low-rank Markov Decision Processes: From Online to Offline RL
Weitong Zhang, Jiafan He, Dongruo Zhou +2
The success of deep reinforcement learning (DRL) lies in its ability to learn a representation that is well-suited for the exploration and exploitation task. To understand how the…
MBRL-Lib: A Modular Library for Model-based Reinforcement Learning
Luis Pineda, Brandon Amos, Amy Zhang +2
Model-based reinforcement learning is a compelling framework for data-efficient learning of agents that interact with the world. This family of algorithms has many subcomponents th…
Model-Invariant State Abstractions for Model-Based Reinforcement Learning
Manan Tomar, Amy Zhang, Roberto Calandra +2
Accuracy and generalization of dynamics models is key to the success of model-based reinforcement learning (MBRL). As the complexity of tasks increases, so does the sample ineffici…
Multi-Task Reinforcement Learning with Context-based Representations
Shagun Sodhani, Amy Zhang, Joelle Pineau
The benefit of multi-task learning over single-task learning relies on the ability to use relations across tasks to improve performance on any single task. While sharing representa…