Search Results for author:
Found 6 papers, 4 papers with code
Making Efficient Use of Demonstrations to Solve Hard Exploration Problems
This paper introduces R2D3, an agent that makes efficient use of demonstrations to solve hard exploration problems in partially observable environments with highly variable initial conditions.
Overcoming catastrophic forgetting in neural networks
The ability to learn tasks in a sequential fashion is crucial to the development of artificial intelligence.
Ranked #3 on
Continual Learning
on F-CelebA (10 tasks)
The Predictron: End-To-End Learning and Planning
One of the key challenges of artificial intelligence is to learn models that are effective in the context of planning.
Alchemy: A benchmark and analysis toolkit for meta-reinforcement learning agents
There has been rapidly growing interest in meta-learning as a method for increasing the flexibility and sample efficiency of reinforcement learning.
Meta-learning of Sequential Strategies
In this report we review memory-based meta-learning as a tool for building sample-efficient strategies that learn from past experience to adapt to any task within a target class.
Explainability Via Causal Self-Talk
We take a pragmatic view of the issue, and define a set of desiderata that capture both the ambitions of XAI and the practical constraints of deep learning.
