34 citations · 51 across the 5 of their papers we have counts for
13 papers
Explainability Via Causal Self-Talk
Nicholas A. Roy, Junkyung Kim, Neil Rabinowitz
Explaining the behavior of AI systems is an important problem that, in practice, is generally avoided. While the XAI community has been developing an abundance of techniques, most…
Alchemy: A benchmark and analysis toolkit for meta-reinforcement learning agents
Jane X. Wang, Michael King, Nicolas Porcel +14
There has been rapidly growing interest in meta-learning as a method for increasing the flexibility and sample efficiency of reinforcement learning. One problem in this area of res…
Should I tear down this wall? Optimizing social metrics by evaluating novel actions
János Kramár, Neil Rabinowitz, Tom Eccles +1
One of the fundamental challenges of governance is deciding when and how to intervene in multi-agent systems in order to impact group-wide metrics of success. This is particularly…
Making Efficient Use of Demonstrations to Solve Hard Exploration Problems
Tom Le Paine, Caglar Gulcehre, Bobak Shahriari +11
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…
Meta-learners' learning dynamics are unlike learners'
Neil C. Rabinowitz
Meta-learning is a tool that allows us to build sample-efficient learning systems. Here we show that, once meta-trained, LSTM Meta-Learners aren't just faster learners than their s…
Meta-learning of Sequential Strategies
Pedro A. Ortega, Jane X. Wang, Mark Rowland +21
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. O…