research
∙
02/09/2023
Scaling Goal-based Exploration via Pruning Proto-goals
One of the gnarliest challenges in reinforcement learning (RL) is explor...
share
research
∙
08/26/2021
When should agents explore?
Exploration remains a central challenge for reinforcement learning (RL)....
research
∙
05/11/2021
Return-based Scaling: Yet Another Normalisation Trick for Deep RL
Scaling issues are mundane yet irritating for practitioners of reinforce...
research
∙
02/26/2020
Policy Evaluation Networks
Many reinforcement learning algorithms use value functions to guide the ...
research
∙
12/14/2019
Adapting Behaviour for Learning Progress
Determining what experience to generate to best facilitate learning (i.e...
research
∙
10/16/2019
Conditional Importance Sampling for Off-Policy Learning
The principal contribution of this paper is a conceptual framework for o...
research
∙
06/07/2019
Non-Differentiable Supervised Learning with Evolution Strategies and Hybrid Methods
In this work we show that Evolution Strategies (ES) are a viable method ...
research
∙
04/25/2019
Ray Interference: a Source of Plateaus in Deep Reinforcement Learning
Rather than proposing a new method, this paper investigates an issue pre...
research
∙
01/30/2019
Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement
The ability to transfer skills across tasks has the potential to scale u...
research
∙
12/18/2018
Universal Successor Features Approximators
The ability of a reinforcement learning (RL) agent to learn about many r...
research
∙
11/16/2018
The Barbados 2018 List of Open Issues in Continual Learning
We want to make progress toward artificial general intelligence, namely ...
research
∙
06/06/2018
Meta Learning by the Baldwin Effect
The scope of the Baldwin effect was recently called into question by two...
research
∙
02/22/2018
Unicorn: Continual Learning with a Universal, Off-policy Agent
Some real-world domains are best characterized as a single task, but for...
research
∙
10/06/2017
Rainbow: Combining Improvements in Deep Reinforcement Learning
The deep reinforcement learning community has made several independent i...
research
∙
08/16/2017
StarCraft II: A New Challenge for Reinforcement Learning
This paper introduces SC2LE (StarCraft II Learning Environment), a reinf...
research
∙
04/12/2017
Deep Q-learning from Demonstrations
Deep reinforcement learning (RL) has achieved several high profile succe...
research
∙
12/28/2016
The Predictron: End-To-End Learning and Planning
One of the key challenges of artificial intelligence is to learn models ...
research
∙
11/16/2016
Reinforcement Learning with Unsupervised Auxiliary Tasks
Deep reinforcement learning agents have achieved state-of-the-art result...
research
∙
06/16/2016
Successor Features for Transfer in Reinforcement Learning
Transfer in reinforcement learning refers to the notion that generalizat...
research
∙
06/14/2016
Learning to learn by gradient descent by gradient descent
The move from hand-designed features to learned features in machine lear...
research
∙
06/06/2016
Unifying Count-Based Exploration and Intrinsic Motivation
We consider an agent's uncertainty about its environment and the problem...
research
∙
01/16/2013
Adaptive learning rates and parallelization for stochastic, sparse, non-smooth gradients
Recent work has established an empirically successful framework for adap...
research
∙
09/26/2012
Efficient Natural Evolution Strategies
Efficient Natural Evolution Strategies (eNES) is a novel alternative to ...
research
∙
06/06/2012
No More Pesky Learning Rates
The performance of stochastic gradient descent (SGD) depends critically ...
research
∙
09/06/2011
Measuring Intelligence through Games
Artificial general intelligence (AGI) refers to research aimed at tackli...
research
∙
06/22/2011
Natural Evolution Strategies
This paper presents Natural Evolution Strategies (NES), a recent family ...
research
∙
06/10/2011